10,478 results on '"*NATURAL language processing"'
Search Results
2. Shortcut Learning of Large Language Models in Natural Language Understanding.
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MENGNAN DU, FENGXIANG HE, NA ZOU, DACHENG TAO, and XIA HU
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LANGUAGE models , *NATURAL language processing , *ARTIFICIAL intelligence , *MACHINE learning , *ALGORITHMS , *INDUCTION (Logic) - Abstract
The article looks at the use of large language models to carry out natural language understanding (NLU) tasks. It suggests that the shortcut learning common to existing large language models based on machine learning limits how robust their performance can be because they are overly dependent on spurious correlations and incidental relationships. It discusses possible approaches to overcoming this problem in the future development of large language models.
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- 2024
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3. Show It or Tell It? Text, Visualization, and Their Combination: When communicating information, language should be considered as co-equal with visualization.
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HEARST, MARTI A.
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DATA visualization , *LANGUAGE & languages , *COMMUNICATION in information science , *LITERACY , *NATURAL language processing , *USER interfaces - Abstract
This article emphasizes the corresponding role language should have, along with visualization, in the communication of information. Topics include the combination and balance of text and visualization, an investigation of text without visualization, necessary improvements to cognitive models, and how natural language processing can impact information communication.
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- 2023
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4. Chatbot application training using natural language processing techniques: Case of small-scale agriculture.
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Ong, R. J., Raof, R. A. A., Sudin, S., and Choong, K. Y.
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CHATBOTS , *DATABASES , *NATURAL language processing , *TACIT knowledge , *AGRICULTURE - Abstract
Tacit knowledge, which is based on first-hand experience and is more difficult to articulate, has evolved alongside natural languages as they are passed down through the years. In computing, Natural Language Processing (or NLP) refers to a set of methods for studying and modelling human languages that may be studied and represented automatically. Extracting or searching through vast bodies of unregulated text for specific information can be a complex and time-consuming process. Knowledge comes in several shapes and sizes, but can usually be differentiated into two types: structured or unstructured. Using NLP techniques, unstructured text data can be translated into a structured and well-organized database and then used for question-answering purposes. This paper is about the implementation of NLP techniques to convert unstructured text data into a structured database for Chatbot application training. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Arabic automatic question generation using transformer model.
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Alhashedi, Saleh Saleh, Suaib, Norhaida Mohd, and Bakri, Aryati
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TRANSFORMER models , *NATURAL language processing , *INTERNET content , *CHILDREN'S books , *ELECTRONIC textbooks , *LINGUISTIC models - Abstract
Students of all ages benefit greatly from the use of questions in the evaluation process and in the improvement of their overall educational outcomes. The educational process's adaptation, shift to online education, and the rapid growth of educational content on the internet. Institutions, schools, and academic organisations struggle to generate exam questions in a timely manner due to the use of the outdated method. Exam question preparation is a complex and time-consuming activity that calls for an in-depth familiarity with the subject matter and the skill to build the questions, both of which grow more challenging as text size increases. Generating questions that are both natural and relevant from a variety of text data inputs, with the possibility to provide an answer, is the goal of automatic question generation (AQG). The Arabic language has seen a small number of contributions to this problem-solving effort. Many existing works rely on Rule-based methods and input text from children's books, stories, or textbooks to manually construct question styles. There is a lack of linguistic diversity in these models, and the tasks get increasingly difficult and time-consuming as the quantity of the text increases. When it comes to Natural Language Processing (NLP), Transformer is one of the most flexible deep-learning models. In this research, we propose a fully-automated Arabic AAQG model built on the Transformer architecture, which can take a single document of limitless length in Arabic and create N questions from it. These questions can be used in educational contexts. Our model achieves performance results with (19.12 BLEU, 23.00 METEOR, and 51.99 ROUGE-L) using mMARCO dataset. [ABSTRACT FROM AUTHOR]
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- 2024
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6. VADER-IT: A sentiment analysis tool for the Italian language.
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Martinis, Maria Chiara, Zucco, Chiara, and Cannataro, Mario
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ITALIAN language , *SENTIMENT analysis , *DEEP learning , *TRANSFORMER models , *MEDICAL writing , *NATURAL language processing , *TEXT recognition - Abstract
"Polarity detection" is a technique used in the field of sentiment analysis in texts, a subset of natural language processing (NLP), which determines the emotional polarity of a text, i.e., whether it expresses a positive, negative, or neutral opinion. Despite the impact registered in the field of Natural Language Processing by Deep Learning techniques and, in particular, by Transformers, these approaches come not without downsides, generally related to low-sources languages and domains. In this article, a lexicon-based approach is applied to extract polarity from medical reviews written in Italian from an Italian portal. Specifically, Vader-IT, an adaptation of the popular VADER sentiment analysis tool tailored for the Italian language, is employed to predict the overall sentiment expressed in 5,491 Italian-language reviews about several Italian hospital departments specialized in heart-related diseases. The reviews are accessible on the Qsalute website urlhttps://www.qsalute.it/. The results show a micro averaged F1 −score = 90.8% and a micro averaged Jaccard −score = 83%. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Song lyrics genre detection using RNN.
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Pasha, Syed Nawaz, Ramesh, Dadi, Mohmmad, Sallauddin, Shabana, Kothandaraman, D., and Sravanthi, T.
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SONG lyrics , *DIGITAL music , *SHORT-term memory , *BIRDSONGS , *LONG-term memory , *NATURAL language processing , *MUSICAL aesthetics - Abstract
Digitalization of music is the new trend, and preferences of individuals are highly rated. Millions of songs are being streamed in the music applications. The companies providing these services need to sort and arrange a wide range of music tastes for all of its users. On top of that, fresh music from various artists in a wide spectrum of genres are popping up every day. To keep track of all this, a classification system can be handy. So, we propose an RNN based model based on Natural Language processing to classify the songs based on their lyrics into different genres [1]. Additionally, this tool can be handy to the music lovers for quickly identifying which genre a particular song belongs to. In this paper, we apply Long Short Term Memory (LSTM) model with both Universal Serial Embedder (USE) and Bert embedders. A comparative study is performed to understand which combination of models works based to classify the genres based on lyrics. From our results, on the basis of accuracy of the model, we found that USE embedder with LSTM [2] gives a slightly better performance than Bert embedder. The LSTM model with USE embedding gave the highest accuracy of 83.42% when trained over a range of five folds. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Automatic essay scoring using NLP.
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Sheshikala, M., Rajesh, Mothe, and Akarapu, Mahesh
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CONVOLUTIONAL neural networks , *NATURAL language processing , *ESSAYS , *ARTIFICIAL intelligence , *MANUAL labor , *TRANSFORMER models - Abstract
It is a known fact that any update in the history of educational sector has always been a positive impact in the livelihood of people towards technology. Our project is one such a kind where rating essays is the major criteria we want to work on. Essay evaluation is considered as a systematic way to give rating to the essays written. Automatic essay scoring is a process of grading essays without human intervention. The computer systems are trained using technical, artificial intelligence architectures where natural language processing comes into picture. The process of making machine resembles to the human intelligence and to work, as if as a human could is the main motive of natural language processing. Under this criterion, we have chosen a part of educational preview to build a system that is capable of rating written work, namely essays. Our project aims to provide a solution that evaluates essays as an automatic process. The basic idea here is to develop a software system that can be beneficial to educational institutions, business organizations, researchers, etc. Automatic essay scoring has a powerful gain over making it work, because it helps in reduction of manual work, gives a scope for every element without bias, also act as a key role in being time-efficient. There are past approaches in finding a way to develop an automated system to score essays using regression analysis, convolution neural networks, while we worked through transformer-based model, named BERT. [ABSTRACT FROM AUTHOR]
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- 2024
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9. A natural language based intelligent banking chatbot.
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Pasha, Nawaz, Ramesh, Dadi, Mohmmad, Sallauddin, Shabana, Dhandapani, Kothandaraman, and Mendu, Mruthyunjaya
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CHATBOTS , *NATURAL language processing , *NATURAL languages , *WEB-based user interfaces , *CALL centers , *DEVELOPMENT banks - Abstract
Chatbot is an intelligent system which simply defines human-to-machine interaction and this chatbots are biggest development trend today. Contacting customer centers or going to bank for banking related queries invests lot of time and human effort, further the customer may get insufficient information and may have uncertainty through this process. This paper aims to convey the development of Banking Chatbot that gives guidance regarding the services provided by bank and provides detailed descriptions to the user's query. This Chatbot is user friendly to communicate with and it is an easy way to get response on time. To overcome the difficulty web application using Natural language processing and neural network is developed [1]. NLP is an added advantage to make chatbot understand user queries. The operations in this Banking Chatbot include viewing beneficiaries and post queries regarding banking services. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Building an NLP based speech recognition technology for emergency call centers.
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Erukala, Sudarshan, Reddy, Prabhakar, Ramesh, Oruganti, Ramesh, Nagaram, Kumar, Atul, Prabhanjan, Bonthala, and Bolukonda, Prashanth
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SPEECH perception , *ARTIFICIAL neural networks , *LANGUAGE models , *CALL centers , *GAUSSIAN mixture models , *NATURAL language processing , *AUTOMATIC speech recognition - Abstract
The approaches of automated speech identification for spoken conversations in emergencies call centres were explored and compared therefore in research. These methodology included acoustic and linguistic models, as well as labelling techniques. Currently present speech recognition algorithms perform poorly because contact centre discussion speech has special context and is spoken in loud, emotional contexts. Consequently, the primary components of speaker verification designs and acoustical training methodologies—as well such Various investigations and analyses of symmetrical information labelling methods were performed. Various variations of Deep Neural Network/Hidden Markov Model (DNN/ HMM) and Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) approaches might have been implemented and tested in order to establish an efficient language framework for conversation information. Furthermore, useful conversation system language models developed Using intrinsic and extrinsic criteria, outlined Finally, when these recommended information labelling techniques with spelling correction are compared with typical labelling techniques, they dominate the other methodologies by a significant proportion. Using the investigation's findings as a guide, we found Showed the use of spelling adjustments prior to training information for a labelling approach, trigram with Kneser-Ney discounting for a language model, and DNN/HMM for an acoustic model are efficient setups for conversation voice recognition in emergency call centres. In order to be clear, this study was Done using two distinct datasets that were gathered from emergency calls: the Dialogue dataset (27 h), which comprises the speech of the call agents, and the Summary dataset (53 h), which contains spoken summaries of those conversations summarising emergency situations. Even if the remarks were taken from the Our strategies are loosely related to particular linguistic aspects despite the fact that the emergency contact centre is in the Turkic language family of Azerbaijani, which is spoken there. As a result, it is expected that the recommended ways will also work with the other languages in the same family. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Developing chat server for addressing FAQ's about creative learning.
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Joshi, Shridhar, Warik, Akshada, Rathod, Harsh, Jha, Jayshree, and Aher, Anagha
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NATURAL language processing , *ONLINE education , *SENTIMENT analysis , *BOTNETS , *TEXT messages , *DATA mining - Abstract
During the pandemic, rapid growth in online learning and educational platforms have been seen. However, the major problem faced by the learners is that they were missing the student-teacher interaction as well as peer-to-peer interaction. To cover up this gap, features like forums, live chat, etc. were introduced. In this project, we will be building a live chat server using Node.js, Express. js, and socket. io. One thing that is observed in these online learning platforms is that the usersmay not always consider the sentiments and feelings of other users present on the live server. To prevent the use of slang, vulgar language, and inappropriate words, we would be using ananalysis bot that will scan for such inappropriate words and if found, the message will be highlighted and will not be uploaded to the server unless corrected. This helps to maintain the quality of chats on the website considering that the website is meant to be a learning platform where users from various backgrounds, age groups, and nationalities will be present. Sentiment analysis, Data Mining, and Natural Language Processing will be used toanalyze the message. The degree of the in appropriateness of the text message will be calculated based on the classification of words done using the Naive-Bayes Classifier. [ABSTRACT FROM AUTHOR]
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- 2024
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12. A systematic review on various applications and challenges in deep learning.
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Yeole, Ashwini N. and S., Guru Prasad M.
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MACHINE learning , *DEEP learning , *NATURAL language processing , *IMAGE recognition (Computer vision) , *COMPUTER vision , *ARTIFICIAL intelligence - Abstract
Deep learning and machine learning are essential since most companies require smart analytics to stay competitive. Artificial intelligence gave rise to machine learning, which in turn gave rise to deep learning. Machine Learning still dominates business analytics with its algorithms, despite the high-end uses of Deep Learning in areas like Computer Vision and Natural Language Processing. By summarizing the numerous machine learning models that are currently available on the market, this survey article demonstrates the learning transition from machine learning to deep learning. It also provides insight into deep learning models and methodologies, as well as the challenges faced and the expected future course of deep learning. A potent, cutting-edge method for analyzing photos, especially remote sensing (RS) images, is deep learning (DL). Remote sensing image scene classification, which attempts to assign semantic categories to remote sensing images based on their contents, has a variety of applications. Thanks to the powerful feature learning capabilities of DNNs, deep learning-based remote sensing image scene categorization has generated a lot of interest and made significant progress. A range of remote sensing applications, such as estimating water availability, monitoring water change over time, and predicting droughts and floods, can benefit from surface water mapping. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Comparison of novel natural language processing algorithm with artificial neural networks in personal assistance to decrease time consumption.
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Dhanushravi, R. and Logu, K.
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NATURAL language processing , *ARTIFICIAL neural networks , *LARGE-scale brain networks , *ALGORITHMS - Abstract
This work is a relative investigation of novel normal language and counterfeit brain networks calculations for improving voice discovery to lessen the time utilization of time individual collaborators. Materials and Methods: Novel Natural Language Processing (NNLP) calculation and Artificial Neural Networks (ANNs) calculation strategies are reenacted by differing the NNLP boundary and mechanize voice location to upgrade the pH. Test size is determined utilizing G power 80% for two gatherings and there are 40 examples utilized in this work. Results: Based on acquired results NNLP has fundamentally diminished time utilization and the exactness has been worked on around 89.00% contrasted with ANNs with precision of 71.90%. Measurable importance contrast among NNLP and ANNs was viewed as 0.376 (p>0.05). End: NNLP calculations give improved brings about voice acknowledgment than ANNs calculations. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Comparison of natural language processing algorithm with support vector machine for fake news identification to improve peak signal to noise ratio with classified accuracy.
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Basha, Shaik Jabeer and Logu, K.
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SUPPORT vector machines , *SIGNAL-to-noise ratio , *NATURAL language processing , *FAKE news , *ALGORITHMS - Abstract
The primary objective is to carry out the identification of phony news discovery around the web-based entertainment with the proposed Natural Language Processing contrasted and Support Vector machine Algorithm. Counterfeit news discovery is executed utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Support Vector Machine(N=10) calculations. Phony and True these two kinds of dataset is utilized for Fake news discovery, and it is gathered from kaggle.com. Dataset comprises of columns and 6 principal boundaries that are connected with the phony news that information gathered from twitter. For each gathering 20 examples are taken, and it is partitioned into preparing and testing. Exactness for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 72.700%. There exists a logical huge distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05 Fake news recognition utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]
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- 2024
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15. A malicious news detection on social networks using natural language processing technique in comparison with deep learning algorithm with improved F1 score.
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Basha, Shaik Jabeer and Logu, K.
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MACHINE learning , *DEEP learning , *SOCIAL networks , *SUPPORT vector machines , *NATURAL language processing , *FAKE news - Abstract
False news is characterized as a made-up story with a goal to bamboozle or to delude. In this paper we present the answer for the errand of fake news discovery by using Deep Learning structures. Fake news identification is carried out utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Deep Learning algorithm(N=10) calculations. False and True these two sorts of dataset is utilized for Fake news recognition, and it is gathered from kaggle.com. Dataset comprises lines and 6 fundamental boundaries that are connected with the False news that information gathered from twitter. For each gathering more than 30 examples are taken, and it is separated into preparing and testing. Accuracy for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 77.500%. There exists an insightful critical distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05. Fake news location utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Implementation of sustainable development goals through literaku application based on Google cloud APIs to improve literacy for blind people.
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Imam, Khairul, Amalia, Amalia, Nasution, Fitri Aulia Fadillah, Martin, Eric, Ghozali, Muhammad, and Siagian, Farhan Doli Fadhiil
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BRAILLE , *CLOUD computing , *SUSTAINABLE development , *NATURAL language processing , *AGILE software development , *SCRUM (Computer software development) - Abstract
Quality education emerges on Sustainable Development Goals or SDGs in point 4 that ensure everyone receives education inclusively and equitably. Blind refers to a condition in which the function of the sense of sight is impaired to varying degrees, ranging from mild to severe to total blindness. The main problem experienced by the visually impaired in accessing literacy is highly limited due to the high cost of producing braille books, the inability of blind individuals to read braille books, and the limited availability of alternative sources, such as audiobooks. Literaku is an Android-based application that allows blind people to independently improve their literacy through the implementation of Google Cloud APIs, which serve as a tool for running applications and have a role in receiving, processing, and executing voice commands from the end user. The Literaku application aims to optimize the use of Indonesian voice commands by understanding the meaning of the nearest word with the support of Natural Language Processing technology to aid the visually impaired in locating readings and performing all application-related tasks by commanding and listening. The method applied the Agile Software Development Life Cycle with the SCRUM framework, which was conducted in phases and iterations. The Literaku application was evaluated by conducting usability testing and surveying users' satisfaction scores. The usability test was performed twice with five blind junior high school students at SLB-A YAPENTRA Tanjung Morawa District to obtain accurate user experience feedback and ensure that the program runs as intended. As a result, the final usability testing of Literaku application reached a success rate of 100%, and the level of participant satisfaction reached 89.60%, representing that the Literaku application was accepted by users very satisfactorily. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Using chatbot for teaching arabic language syntax.
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Hussien, Nadia Mahmood, Mohialden, Yasmin Makki, Hussien, Kawakib Mahmood, and Joshi, Kapil
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CHATBOTS , *NATURAL language processing , *ARABIC language , *MACHINE learning , *KNOWLEDGE base , *SYNTAX (Grammar) - Abstract
Chatbots are being used in a wide variety of industries, ranging from industry to education. It is not as effective when using traditional ways of developing a chatbot system as it is when using machine learning (ML). Historically, they were created using finite-state machines, rule-based systems, and knowledge bases. Although these technologies had shortcomings, they were nonetheless employed to create chatbots. This is because natural language processing and neural network technology have simplified the task of conversational AI systems categorizing intentions and locating persons and places. Many people have asked us how we created an Arabic chatbot that understands real language, and we'd like to demonstrate how we achieved it. It is capable of responding, acting on behalf of the user, and retaining the context of a communication between two persons. We employed models such as FastText and BERT, which may be used in multiple languages concurrently. Additionally, we employed two pipeline components that we created specifically for this project. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Voice in the Machine: Ethical Considerations for Language-Capable Robots: Parsing the promise of language-capable robots.
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Williams, T., Matuszek, Cynthia, Jokinen, Kristiina, Korpan, Raj, Pustejovsky, James, and Scassellati, Brian
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ARTIFICIAL intelligence & ethics , *ETHICS , *COMPUTATIONAL linguistics , *NATURAL language processing , *DISCRIMINATION (Sociology) , *PREJUDICES - Abstract
The article discusses various ethical considerations for language-capable robots. These concerns include trust, influence, identity, and privacy, and will require consideration by researchers, practitioners, and the general public. Various potential negative outcomes are discussed including robot control over human morals, a default identity perception grounded in white heteropatriarchy, gendered and racialized language-capable robots, and the potential for robots to be used as mobile surveillance tools.
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- 2023
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19. A Computational Inflection for Scientific Discovery.
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HOPE, TOM, DOWNEY, DOUG, ETZIONI, OREN, WELD, DANIEL S., and HORVITZ, ERIC
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SCIENTIFIC knowledge , *LANGUAGE models , *SCIENTIFIC method , *ARTIFICIAL intelligence , *INFORMATION retrieval , *NATURAL language processing , *COGNITION , *HUMAN-artificial intelligence interaction - Abstract
This article presents an overview on task-guided scientific knowledge retrieval as a way for researchers to overcome the limitations of human cognitive capacity that in the age of explosive digital information creates a cognitive bottleneck. Topics include prototypes of task-guided scientific knowledge retrieval, as well as a look at novel representations, tools, and services and a review of systems that aid researchers in all aspects of scientific inquiry and discovery.
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- 2023
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20. Analogous Forecasting for Predicting Sport Innovation Diffusion: From Business Analytics to Natural Language Processing.
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Wanless, Liz and Naraine, Michael L.
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NATURAL language processing , *SPORTS forecasting , *DIFFUSION of innovations , *BUSINESS analytics , *DIFFUSION of innovations theory , *HOCKEY players , *FUTUROLOGISTS - Abstract
The purpose of this study was to analyze the diffusion of one sport innovation to forecast a second. Contextualized within the diffusion of innovations theory, this study investigated cumulative business analytics diffusion as an analog for cumulative natural language processing (NLP) diffusion in professional sport. A total of 89 teams of the 123 teams in the Big Four North American men's professional sport leagues contributed: 21 from the National Football League, 23 from the National Basketball Association, 22 from Major League Baseball, and 23 from the National Hockey League. Utilizing an analogous forecasting approach, a discrete derivation of the Bass model was applied to cumulative BA adoption data. Parameters were then extended to predict cumulative NLP adoption. Resulting BA-estimated parameters (p =.0072, q =.3644) determined a close fit to NLP diffusion (root mean square error of approximation = 3.51, mean absolute error = 2.98), thereby validating BA to predict the takeoff and full adoption of NLP. This study illuminates an ongoing and isomorphic process for diffusion of innovations in the professional sport social system and generates a novel application of diffusion of innovations theory to the sport industry. [ABSTRACT FROM AUTHOR]
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- 2023
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21. Innovation trends and evolutionary paths of green fuel technologies in maritime field: A global patent review.
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Sun, Minghan, Tong, Tong, Jiang, Man, and Zhu, Jewel X.
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GREEN fuels , *HYDROGEN as fuel , *FUEL cells , *HYBRID power systems , *GREEN technology , *NATURAL language processing , *ALTERNATIVE fuels - Abstract
As global environmental issues become increasingly prominent, the maritime industry faces an urgent imperative to curtail carbon emissions and mitigate environmental impact. Green marine alternative fuels are actively addressing these challenges. Global patent data are collected, and four sub-technologies of green fuel technologies in the maritime field are extracted based on Natural Language Processing. To evaluate the innovation trends and evolutionary paths of these technologies, the Main Path Analysis is employed to identify the evolution path of technologies and the evolution situation of major innovative entities, while the Social Network Analysis was used to present the results visually. These four sub-technologies (Hydrogen & Fuel Cell, Methanol & Ethanol, Ammonia, and LNG & LPG) have occupied the mainstream of fuel technology in maritime over the past five years, with 34.6% of patents, 38.3% of patent citations, and 93.9% of technological influence, accounting for 27.4%, 20.5%, 16.7% and 44.3%, respectively. Ammonia, and Hydrogen & Fuel Cell are springing up like mushrooms after rain, while the development of LNG & LPG, Methanol & Ethanol is relatively mature. Each of these technologies showcases distinct developmental paradigms. We elaborated on developmental paradigms and future research priorities of each technology in the conclusion. In addition, new technological opportunities are also created through the cross-fertilization of technologies. In particular, integrated systems for hydrogen production, storage, and combustion on LNG ships, and maritime hybrid power systems driven by ammonia and hydrogen have opened a new window for the green development of maritime fuels. • Four green fuel technologies account for 93.9% of maritime fuel technological influence in the past five years. • Hydrogen & Fuel Cell and Ammonia rapidly expand, while LNG & LPG and Methanol & Ethanol are relatively mature. • This study details the unique development paradigms and future research priorities for each technology. • Hydrogen systems on LNG ships and hybrid ammonia-hydrogen maritime power open new avenues for green fuel development. [ABSTRACT FROM AUTHOR]
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- 2024
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22. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.
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Zuo, Xu, Kumar, Ashok, Shen, Shuhan, Li, Jianfu, Cong, Grace, Jin, Edward, Chen, Qingxia, Warner, Jeremy L., Yang, Ping, and Xu, Hua
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TREATMENT effectiveness , *LANGUAGE models , *NATURAL language processing , *ELECTRONIC health records , *DATA mining , *CANCER treatment - Abstract
PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics. Extracting systemic anticancer therapy and RECIST response information from clinical notes. [ABSTRACT FROM AUTHOR]
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- 2024
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23. Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain.
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Dennstädt, Fabio, Zink, Johannes, Putora, Paul Martin, Hastings, Janna, and Cihoric, Nikola
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LANGUAGE models , *MEDICAL literature , *LITERATURE reviews , *TECHNOLOGICAL innovations , *LIKERT scale , *NATURAL language processing - Abstract
Background: Systematically screening published literature to determine the relevant publications to synthesize in a review is a time-consuming and difficult task. Large language models (LLMs) are an emerging technology with promising capabilities for the automation of language-related tasks that may be useful for such a purpose. Methods: LLMs were used as part of an automated system to evaluate the relevance of publications to a certain topic based on defined criteria and based on the title and abstract of each publication. A Python script was created to generate structured prompts consisting of text strings for instruction, title, abstract, and relevant criteria to be provided to an LLM. The relevance of a publication was evaluated by the LLM on a Likert scale (low relevance to high relevance). By specifying a threshold, different classifiers for inclusion/exclusion of publications could then be defined. The approach was used with four different openly available LLMs on ten published data sets of biomedical literature reviews and on a newly human-created data set for a hypothetical new systematic literature review. Results: The performance of the classifiers varied depending on the LLM being used and on the data set analyzed. Regarding sensitivity/specificity, the classifiers yielded 94.48%/31.78% for the FlanT5 model, 97.58%/19.12% for the OpenHermes-NeuralChat model, 81.93%/75.19% for the Mixtral model and 97.58%/38.34% for the Platypus 2 model on the ten published data sets. The same classifiers yielded 100% sensitivity at a specificity of 12.58%, 4.54%, 62.47%, and 24.74% on the newly created data set. Changing the standard settings of the approach (minor adaption of instruction prompt and/or changing the range of the Likert scale from 1–5 to 1–10) had a considerable impact on the performance. Conclusions: LLMs can be used to evaluate the relevance of scientific publications to a certain review topic and classifiers based on such an approach show some promising results. To date, little is known about how well such systems would perform if used prospectively when conducting systematic literature reviews and what further implications this might have. However, it is likely that in the future researchers will increasingly use LLMs for evaluating and classifying scientific publications. [ABSTRACT FROM AUTHOR]
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- 2024
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24. Hybrid mutation driven testing for natural language inference.
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Meng, Linghan, Li, Yanhui, Chen, Lin, Ma, Mingliang, Zhou, Yuming, and Xu, Baowen
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Summary Natural language inference (NLI) is a task to infer the relationship between the premise and hypothesis sentences, whose models have essential applications in the many natural language processing (NLP) fields, for example, machine reading comprehension and recognizing textual entailment. Due to the data‐driven programming paradigm, bugs inevitably occur in NLI models during the application process, which calls for novel automatic testing techniques to deal with NLI testing challenges. The main difficulty in achieving automatic testing for NLI models is the oracle problem; that is, it may be too expensive to label NLI model inputs manually and hence be too challenging to verify the correctness of model outputs. To tackle the oracle problem, this study proposes a novel automatic testing method
hybrid mutation driven testing (HMT) , which extends the mutation idea applied in other NLP domains successfully. Specifically, as there are two sets of sentences, that is, premise and hypothesis, to be mutated, we propose four mutation operators to achieve the hybrid mutation strategy, which mutate the premise and the hypothesis sentences jointly or individually. We assume that the mutation would not affect the outputs; that is, if the original and mutated outputs are inconsistent, inconsistency bugs could be detected without knowing the true labels. To evaluate our method HMT, we conduct experiments on two widely used datasets with two advanced models and generate more than 520,000 mutations by applying our mutation operators. Our experimental results show that (a) our method, HMT, can effectively generate mutated testing samples, (b) our method can effectively trigger the inconsistency bugs of the NLI models, and (c) all four mutation operators can independently trigger inconsistency bugs. [ABSTRACT FROM AUTHOR]- Published
- 2024
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25. Research on factors influencing the consumer repurchase intention: Data mining of consumers' online reviews based on machine learning.
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Zhang, Jianming, Zheng, Hao, Liu, Jie, and Shen, Wei
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CONSUMER behavior , *CONSUMERS' reviews , *DATA mining , *THEORY of reasoned action , *QUALITY of service , *NATURAL language processing - Abstract
The fierce competition in the market makes it necessary for enterprises to not only consider how to increase consumers' purchase intention but also study to maintain high customer loyalty for continuous purchases. Taking the smartphone brands on the Jingdong platform (hereafter referred to as JD) as an example, the study collected 60,000 review data and using NLP technology for data mining, factors that may affect consumers' willingness to repurchase were extracted. Based on Theory of Reasoned Action (TRA), the questionnaire was made for empirical research. The results showed that the four factors, product attributes, service quality, brand image and price significantly affect consumers' repurchase intention, while service quality had the strongest effect among them, implications of the research are discussed. [ABSTRACT FROM AUTHOR]
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- 2024
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26. A data-driven conceptual framework for understanding the nature of hazards in railway accidents.
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Hong, Wei-Ting, Clifton, Geoffrey, and Nelson, John D.
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RAILROAD accidents , *AIRCRAFT accidents , *TRANSPORTATION safety measures , *NATURAL language processing , *HAZARD mitigation , *HAZARDS - Abstract
Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents. • A framework HazardMap is developed for mapping hazards in the railway system. • A case study of the risk at level crossing is implemented. • Opportunities for practitioners in learning across jurisdiction and time. • Enabling knowledge accumulated to be summarised for policymaking process. [ABSTRACT FROM AUTHOR]
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- 2024
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27. Impacts of regional socioeconomic statuses and global events on solid waste research reflected in six waste-focused journals.
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Zhang, Zhibo, Wang, Jingyi, Li, Jiuwei, Wang, Yao, Yin, Ke, and Fei, Xunchang
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SOLID waste , *TECHNOLOGY assessment , *SOCIOECONOMIC status , *TECHNOLOGICAL innovations , *SOCIOECONOMIC factors - Abstract
[Display omitted] • An innovative framework for categorizing solid waste research (SWR) publications. • SWR trends based on 17,629 publications using Source Latent Dirichlet Allocation. • Correlated SWR trends and major global events since 1990. • Identified influencing socio-economic factors on SWR trends. The research pertaining to solid waste is undergoing extensive advancement, thereby necessitating a consolidation and analysis of its research trajectories. The existing biblio-studies on solid waste research (SWR) lack thorough analyses of the factors influencing its trends. This article presents an innovative categorization framework that categorizes publications from six SWR journals utilizing Source Latent Dirichlet Allocation. First analyse changes in publication numbers across main categories, subcategories, journals, and regions, providing a macro-level study of SWR. Temporal analysis of keywords supplements a micro-level study of SWR, which highlights that emerging technologies with low Technology Readiness Level receive significant attention, while studies on widespread technologies are diminishing. Additionally, this study demonstrates the substantial influence of socioeconomic factors and previous SWR publications on current and future SWR trends. Finally, the article confirms the impact of global events on SWR trends by examining the structural breakpoints of SWR and their correlation with global events. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.
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Tyagin, Ilya and Safro, Ilya
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KNOWLEDGE graphs , *BENCHMARKING (Management) , *NATURAL language processing , *HYPOTHESIS , *SCIENTIFIC discoveries , *SEMANTICS - Abstract
Background: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. Results: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. Conclusions: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport. [ABSTRACT FROM AUTHOR]
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- 2024
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29. TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature.
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Nédellec, Claire, Sauvion, Clara, Bossy, Robert, Borovikova, Mariya, and Deléger, Louise
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WHEAT breeding , *NATURAL language processing , *BIOLOGICAL classification , *LANGUAGE models , *SCIENTIFIC literature , *PHENOTYPES , *WHEAT - Abstract
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature. [ABSTRACT FROM AUTHOR]
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- 2024
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30. Text-to-video generative artificial intelligence: sora in neurosurgery.
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Mohamed, Ali A. and Lucke-Wold, Brandon
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GENERATIVE artificial intelligence , *LANGUAGE models , *NATURAL language processing , *COMPUTER vision , *ARTIFICIAL intelligence - Abstract
Artificial intelligence (AI) has increased in popularity in neurosurgery, with recent interest in generative AI algorithms such as the Large Language Model (LLM) ChatGPT. Sora, an innovation in generative AI, leverages natural language processing, deep learning, and computer vision to generate impressive videos from text prompts. This new tool has many potential applications in neurosurgery. These include patient education, public health, surgical training and planning, and research dissemination. However, there are considerable limitations to the current model such as physically implausible motion generation, spontaneous generation of subjects, unnatural object morphing, inaccurate physical interactions, and abnormal behavior presentation when many subjects are generated. Other typical concerns are with respect to patient privacy, bias, and ethics. Further, appropriate investigation is required to determine how effective generative videos are compared to their non-generated counterparts, irrespective of any limitations. Despite these challenges, Sora and other iterations of its text-to-video generative application may have many benefits to the neurosurgical community. [ABSTRACT FROM AUTHOR]
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- 2024
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31. Quantifying social capital creation in post‐disaster recovery aid in Indonesia: methodological innovation by an AI‐based language model.
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Marutschke, Daniel Moritz, Nurdin, Muhammad Riza, and Hirono, Miwa
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Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]
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- 2024
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32. Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection.
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Almudaifer, Abdullateef I., Covington, Whitney, Hairston, JaMor, Deitch, Zachary, Anand, Ankit, Carroll, Caleb M., Crisan, Estera, Bradford, William, Walter, Lauren A., Eaton, Ellen F., Feldman, Sue S., and Osborne, John D.
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OPIOID abuse , *CLINICAL medicine , *NATURAL language processing , *TRANSFORMER models , *ARCHITECTURAL design - Abstract
Background: The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers of clinical entities involve regular expression or features weights that are trained independently for each modifier. Methods: We develop and evaluate a multi-task transformer architecture design where modifiers are learned and predicted jointly using the publicly available SemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that contains modifiers shared with SemEval as well as novel modifiers specific for OUD. We evaluate the effectiveness of our multi-task learning approach versus previously published systems and assess the feasibility of transfer learning for clinical entity modifiers when only a portion of clinical modifiers are shared. Results: Our approach achieved state-of-the-art results on the ShARe corpus from SemEval 2015 Task 14, showing an increase of 1.1% on weighted accuracy, 1.7% on unweighted accuracy, and 10% on micro F1 scores. Conclusions: We show that learned weights from our shared model can be effectively transferred to a new partially matched data set, validating the use of transfer learning for clinical text modifiers. [ABSTRACT FROM AUTHOR]
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- 2024
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33. NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.
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Wang, Rongshu and Chen, Jianhua
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MACHINE translating , *NUCLEOTIDE sequencing , *NATURAL language processing , *RECURRENT neural networks , *NATURAL languages - Abstract
Backgrounds: The single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long reads algorithmically with the help of the homologous high-precision and low-cost short reads from the Next Generation Sequencing (NGS) technology. Methods: In this work, a hybrid error correction method (NmTHC) based on a generative neural machine translation model is proposed to automatically capture discrepancies within the aligned regions of long reads and short reads, as well as the contextual relationships within the long reads themselves for error correction. Akin to natural language sequences, the long read can be regarded as a special "genetic language" and be processed with the idea of generative neural networks. The algorithm builds a sequence-to-sequence(seq2seq) framework with Recurrent Neural Network (RNN) as the core layer. The before and post-corrected long reads are regarded as the sentences in the source and target language of translation, and the alignment information of long reads with short reads is used to create the special corpus for training. The well-trained model can be used to predict the corrected long read. Results: NmTHC outperforms the latest mainstream hybrid error correction methods on real-world datasets from two mainstream platforms, including PacBio and Nanopore. Our experimental evaluation results demonstrate that NmTHC can align more bases with the reference genome without any segmenting in the six benchmark datasets, proving that it enhances alignment identity without sacrificing any length advantages of long reads. Conclusion: Consequently, NmTHC reasonably adopts the generative Neural Machine Translation (NMT) model to transform hybrid error correction tasks into machine translation problems and provides a novel perspective for solving long-read error correction problems with the ideas of Natural Language Processing (NLP). More remarkably, the proposed methodology is sequencing-technology-independent and can produce more precise reads. [ABSTRACT FROM AUTHOR]
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- 2024
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34. Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia.
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Lopes da Cunha, Pamela, Ruiz, Fabián, Ferrante, Franco, Sterpin, Lucas Federico, Ibáñez, Agustín, Slachevsky, Andrea, Matallana, Diana, Martínez, Ángela, Hesse, Eugenia, and García, Adolfo M.
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ALZHEIMER'S disease , *FREEDOM of speech , *EXECUTIVE function , *FRONTOTEMPORAL dementia , *COGNITIVE testing , *NATURAL language processing - Abstract
Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring. [ABSTRACT FROM AUTHOR]
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- 2024
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35. Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.
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Miranda, Oshin, Kiehl, Sophie Marie, Qi, Xiguang, Brannock, M. Daniel, Kosten, Thomas, Ryan, Neal David, Kirisci, Levent, Wang, Yanshan, and Wang, LiRong
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NATURAL language processing , *POST-traumatic stress disorder , *ELECTRONIC health records , *LANGUAGE models , *MEDICAL needs assessment , *IDENTIFICATION , *MEDICAL record databases - Abstract
Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. Methods: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. Results: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Unraveling minnan imagery: a comprehensive analysis of traditional and modern minnan nursery rhymes through complex networks.
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Wu, Hongrun, Zhang, Lei, Huang, Zheming, Chen, Zhixin, Yang, Weizhong, Tong, Xianqun, Shen, Yiling, Lin, Baozhu, and Li, Shunxing
- Abstract
Nursery rhymes provide insights into the traditions, beliefs, and values of a culture, thereby making it an integral part of a community's heritage. As representative linguistic resources within the intangible cultural heritage of the Hoklo people, Minnan nursery rhymes (MNRs) play a crucial role in connecting the Chinese mainland, Taiwan Strait, and overseas Chinese communities. This study delves into features of 617 traditional and 289 modern pieces through text mining techniques, including text segmentation, the TF-IDF (term frequency-inverse document frequency) method, and the complex network analysis. We examine the frequency and emotional purity of lyrics at a larger scale than previous studies using a small set of manually annotated samples. Furthermore, we analyze the patterns of MNRs by assessing the overall, individual, core-periphery structures of the constructed MNR networks, considering key terms as nodes and co-occurrence relationships between nodes as links. Our investigation reveals the heterogeneous nature of terms in both traditional and modern MNR networks. Moreover, through the community detection method, we identify five primary imagery features presented in MNRs. Traditional MNRs place emphasis on family relationships, folk culture, and food culture, reflecting enduring aspects of Minnan cultural heritage. In contrast, modern MNRs pivot towards themes of children's emotions and natural scenery, indicative of evolving societal values. This study represents the first large-scale complex network analysis of MNRs, providing valuable insights into the embedded Minnan culture and serving as a foundation for further research into the societal dynamics reflected in these cherished MNRs resources. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Diagnostics Based Patient Classification for Clinical Decision Support Systems.
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Paliwal, Gaurav, Bunglowala, Aaquil, and Kanthed, Pravesh
- Abstract
The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines. [ABSTRACT FROM AUTHOR]
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- 2024
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38. Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation.
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Bonnechère, Bruno
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HEALTH literacy , *DATA security , *DATABASE management , *INTERPROFESSIONAL relations , *DIFFUSION of innovations , *HUMAN services programs , *REHABILITATION , *ARTIFICIAL intelligence , *NATURAL language processing , *DECISION making in clinical medicine , *PHYSICAL medicine , *COMMUNICATION , *RESEARCH methodology , *PEOPLE with disabilities , *WELL-being - Abstract
Rehabilitation is a vital component of health care, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a "black box," with complexities that pose challenges for comprehensive analysis and optimization. The emergence of large language models offers promising solutions to better understand this "black box." Large language models excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the "black box." Large language models enhance data integration, communication, assessment, and prediction. This article delves into the ground-breaking use of large language models as a tool to further understand the rehabilitation process. Large language models address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucialwhen deploying large language models. Integrating large language models into rehabilitation yields insights into this intricate process, enhancing data-driven decision making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, large language models represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration. [ABSTRACT FROM AUTHOR]
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- 2024
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39. Shakespeare Machine: New AI-Based Technologies for Textual Analysis.
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Ehrett, Carl, Ghita, Lucian, Ranwala, Dillon, and Menezes, Alison
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LANGUAGE models , *NATURAL language processing , *CONTENT analysis , *METADATA , *PATTERN matching , *PATTERNS (Mathematics) , *HANDWRITING recognition (Computer science) - Abstract
This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare's works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. [ABSTRACT FROM AUTHOR]
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- 2024
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40. Understanding poetry using natural language processing tools: a survey.
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Sisto, Mirella De, Hernández-Lorenzo, Laura, Rosa, Javier De la, Ros, Salvador, and González-Blanco, Elena
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NATURAL language processing , *POETRY (Literary form) - Abstract
Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these tools and traditional scholars investigating poetry and metrics is often missing. The purpose of this article is to bridge this gap by providing a comprehensive survey of the automatic poetry analysis tools available for European languages. The tools are described and classified according to the language for which they are primarily developed, and to their functionalities and purpose. Particular attention is given to those that have open-source code or provide an online version with the same functionality. Combining more traditional research with these tools has clear advantages: it provides the opportunity to address theoretical questions with the support of large amounts of data; also, it allows for the development of new and diversified approaches. [ABSTRACT FROM AUTHOR]
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- 2024
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41. Accelerating Massively Distributed Deep Learning Through Efficient Pseudo-Synchronous Update Method.
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Wen, Yingpeng, Qiu, Zhilin, Zhang, Dongyu, Huang, Dan, Xiao, Nong, and Lin, Liang
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DEEP learning , *NATURAL language processing , *IMAGE recognition (Computer vision) , *STATISTICAL smoothing , *PARALLEL programming - Abstract
In recent years, deep learning models have been successfully applied to large-scale data analysis, including image classification, video caption, natural language processing, etc. Large-scale data analyses take advantage of parallel computing to accelerate the speed of model training, in which data parallelism has become the dominant method for deep learning model training due to its high throughput rate. Synchronous stochastic gradient descent optimization becomes a well-recognized optimization method to ensure model convergence, but the overhead of gradients synchronization increases linearly as the number of workers increases, causing a huge waste of time. Although some efficiency-first asynchronous methods have been proposed, these methods cannot guarantee their convergence in large-scale distributed training. To solve this problem, we propose an efficient pseudo-synchronous approach that updates the network with the previous gradient, performing the synchronization of a new gradient to overlap computation and synchronization. This idea will obviously affect the normal convergence of the model, so we propose a novel adaptive exponential smoothing predicted gradient algorithm for model optimization, which can adaptively adjust the confidence coefficient of the history gradient to ensure the normal convergence of the training process. Experiments prove that our method can speed up the training process and achieve a comparable accuracy rate with standard synchronous SGD. Besides, our method has more efficient weak scalability compared to the traditional synchronous SGD and those in previous related work. We apply our methods to image recognition and video caption applications at most 12288 cores with strong scalability on Tianhe II. Evaluations show that, when configured appropriately, our method attains near-linear scalability using 128 nodes. We get 93.4% weak scaling efficiency on 64 nodes, 90.5% on 128 nodes. [ABSTRACT FROM AUTHOR]
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- 2024
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42. The impact of alcohol on affiliative verbal behavior: A systematic review and meta‐analysis.
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Goodwin, Madeline E. and Sayette, Michael A.
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PHONOLOGICAL awareness , *META-analysis , *QUANTITATIVE research , *NATURAL language processing , *DESCRIPTIVE statistics , *SYSTEMATIC reviews , *SOCIAL skills , *ALCOHOL drinking , *CONFIDENCE intervals , *QUALITY assurance , *HEALTH outcome assessment , *VERBAL behavior , *PSYCHOLOGY information storage & retrieval systems , *SELF-disclosure - Abstract
Background: Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta‐analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions. Methods: PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random‐effects meta‐analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior. Results: Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta‐analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects. Conclusions: This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed. [ABSTRACT FROM AUTHOR]
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- 2024
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43. Visual Analytics for Fine‐grained Text Classification Models and Datasets.
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Battogtokh, M., Xing, Y., Davidescu, C., Abdul‐Rahman, A., Luck, M., and Borgo, R.
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VISUAL analytics , *TEXT mining , *NATURAL language processing , *CLASSIFICATION , *DEBUGGING , *MODEL validation - Abstract
In natural language processing (NLP), text classification tasks are increasingly fine‐grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse‐grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design‐and‐evaluation process to characterize and tackle the growing requirements in their workflow of developing fine‐grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine‐grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation. [ABSTRACT FROM AUTHOR]
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- 2024
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44. Federated Learning in the Detection of Fake News Using Deep Learning as a Basic Method.
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Machová, Kristína, Mach, Marián, and Balara, Viliam
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FEDERATED learning , *DEEP learning , *FAKE news , *MACHINE learning , *NATURAL language processing , *CONVOLUTIONAL neural networks - Abstract
This article explores the possibilities for federated learning with a deep learning method as a basic approach to train detection models for fake news recognition. Federated learning is the key issue in this research because this kind of learning makes machine learning more secure by training models on decentralized data at decentralized places, for example, at different IoT edges. The data are not transformed between decentralized places, which means that personally identifiable data are not shared. This could increase the security of data from sensors in intelligent houses and medical devices or data from various resources in online spaces. Each station edge could train a model separately on data obtained from its sensors and on data extracted from different sources. Consequently, the models trained on local data on local clients are aggregated at the central ending point. We have designed three different architectures for deep learning as a basis for use within federated learning. The detection models were based on embeddings, CNNs (convolutional neural networks), and LSTM (long short-term memory). The best results were achieved using more LSTM layers (F1 = 0.92). On the other hand, all three architectures achieved similar results. We also analyzed results obtained using federated learning and without it. As a result of the analysis, it was found that the use of federated learning, in which data were decomposed and divided into smaller local datasets, does not significantly reduce the accuracy of the models. [ABSTRACT FROM AUTHOR]
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- 2024
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45. Transferring Learned Behaviors between Similar and Different Radios.
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Muller, Braeden P., Olds, Brennan E., Wong, Lauren J., and Michaels, Alan J.
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COMPUTER vision , *RADIO frequency , *CLASSIFICATION algorithms , *AUTOMATIC classification , *SITUATIONAL awareness , *NATURAL language processing - Abstract
Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC. [ABSTRACT FROM AUTHOR]
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- 2024
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46. Exploring the Use of Natural Language Processing to Understand Emotions of Trainees and Faculty Regarding Entrustable Professional Activity Assessments.
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Johnson, Devin, Chopra, Sonaina, and Bilgic, Elif
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NATURAL language processing , *EMOTIONS , *SURGERY , *POISSON regression , *SENTIMENT analysis , *WORD recognition , *WORD frequency - Abstract
Background In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. Objective To explore the application of NLP techniques to understand resident and faculty emotions related to entrustable professional activity (EPA) assessments. Methods Open-ended text data from a survey on emotions toward EPA assessments were analyzed. Respondents were residents and faculty from pediatrics (Peds), general surgery (GS), and emergency medicine (EM), recruited for a larger emotions study in 2023. Participants wrote about their emotions related to receiving/completing EPA assessments. We analyzed the frequency of words rated as positive via a validated sentiment lexicon used in NLP studies. Specifically, we were interested if the count of positive words varied as a function of group membership (faculty, resident), specialty (Peds, GS, EM), gender (man, woman, nonbinary), or visible minority status (yes, no, omit). Results A total of 66 text responses (30 faculty, 36 residents) contained text data useful for sentiment analysis. We analyzed the difference in the count of words categorized as positive across group, specialty, gender, and being a visible minority. Specialty was the only category revealing significant differences via a bootstrapped Poisson regression model with GS responses containing fewer positive words than EM responses. Conclusions By analyzing text data to understand emotions of residents and faculty through an NLP approach, we identified differences in EPA assessment-related emotions of residents versus faculty, and differences across specialties. [ABSTRACT FROM AUTHOR]
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- 2024
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47. Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing.
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Gasmi, Karim, Ayadi, Hajer, and Torjmen, Mouna
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IMAGE retrieval , *CONVOLUTIONAL neural networks , *DIAGNOSTIC imaging , *NATURAL language processing , *IMAGE recognition (Computer vision) - Abstract
In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
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- 2024
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48. Next-Generation Healthcare: Artificial Intelligence Applications in Disease Management.
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Akbulut, Sami and Colak, Cemil
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ARTIFICIAL intelligence , *DISEASE management , *MACHINE learning , *NATURAL language processing - Abstract
This document discusses the application of artificial intelligence (AI) in healthcare, specifically in disease management. The use of AI in healthcare is transforming traditional systems, leading to improved operational efficiency and more accurate diagnoses. The document highlights various AI applications in disease management, including the diagnosis of blood cancers, infectious disorders, diabetic macular edema, oral squamous cell carcinoma, drug-induced hepatotoxicity, mandibular length prediction, appendicitis diagnosis, diabetes detection, brain tumor detection, and inflammatory bowel disease. Additionally, AI is being explored in other areas of medicine, such as drug discovery, robotic surgery, personalized medicine, medical imaging analysis, and epidemic prediction and prevention. However, there are challenges and ethical considerations to address, including data privacy and security, algorithmic bias, transparency and explainability, and regulatory frameworks. Despite these challenges, the potential of AI to revolutionize healthcare is significant, leading to improved patient outcomes and personalized medicine. [Extracted from the article]
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- 2024
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49. Calificación profesional y transiciones laborales en el Brasil.
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ADAMCZYK, Willian, EHRL, Philipp, and MONASTERIO, Leonardo
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UNEMPLOYMENT , *JOB qualifications , *EMPLOYMENT discrimination , *NATURAL language processing , *COGNITIVE learning , *MACHINE learning - Abstract
Resumen: En este artículo se presentan dos métodos para obtener medidas de calificación profesional y distancias ocupacionales de comparabilidad internacional, basados en técnicas de aprendizaje automático y de procesamiento del lenguaje natural. Con estas medidas se generan hechos descriptivos sobre las transiciones laborales y la distribución salarial en el Brasil, tras analizar todos los contratos de trabajo formales registrados en el periodo 2003‐2018. Los trabajadores que utilizan intensivamente competencias cognitivas no rutinarias obtienen mejores resultados en cuanto al empleo, los salarios y el cambio de ocupación. Tras la crisis económica brasileña de 2014, se observan indicios de cambio tecnológico con sesgo de rutina y de polarización del empleo. [ABSTRACT FROM AUTHOR]
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- 2024
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50. Implicit Stance Detection with Hashtag Semantic Enrichment.
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Dong, Li, Su, Zinao, Fu, Xianghua, Zhang, Bowen, and Dai, Genan
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LANGUAGE models , *MICROBLOGS , *NATURAL language processing , *SOCIAL media , *REPRESENTATIONS of graphs , *SOCIAL computing , *INFORMATION retrieval - Abstract
Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task of target-oriented, sentence-level text classification. On popular social media platforms like Twitter, users often express their opinions through hashtags in addition to textual content within tweets. However, current methods primarily treat hashtags as data retrieval labels, neglecting to effectively utilize the semantic information they carry. In this paper, we propose a large language model knowledge-enhanced stance detection framework (LKESD) for stance detection. LKESD contains three main components: an instruction-prompted background knowledge acquisition module (IPBKA) that retrieves background knowledge of hashtags by providing handcrafted prompts to large language models (LLMs); a graph convolutional feature-enhancement module (GCFEM) is designed to extract the semantic representations of words that frequently co-occur with hashtags in the dataset by leveraging textual associations; an a knowledge fusion network (KFN) is proposed to selectively integrate graph representations and LLM features using a prompt-tuning framework. Extensive experimental results on three benchmark datasets demonstrate that our LKESD method outperforms 2.7% on all setups over compared methods, validating its effectiveness in stance detection tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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