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As the processing power of mobile terminals increases, wireless network applications such as voice assistants can put more context-sensitive tasks on the mobile terminals, thus reducing the wireless network bandwidth needed and the cost of data storage in the cloud. Co-reference annotation, identifying the same semantics in context, is one of the critical techniques in these tasks. However, there are some problems with the existing co-reference annotation standards. First, the annotation is incomplete. Second, the types of annotated mentions are inconsistent. Third, there are currently no metrics for the above characteristics. Analyzing the above-mentioned issues, this paper proposes a new co-reference annotation standard. The new standard can annotate more semantics and co-reference relations and only adopts two types of mentions for annotation. Meanwhile, this paper presents a performance evaluation corpus and designs three performance metrics for evaluating the new standard according to the completeness of semantic annotation, the completeness of co-reference annotation, and the consistency of mention. The experiment shows that the new standard outperforms all the baseline methods and achieves 0.95 in the completeness of semantic annotation, 0.68 in the completeness of co-reference annotation, and 0.57 in the consistency of types of mentions. [ABSTRACT FROM AUTHOR]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

Requirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic contexts. In recent years, with the development of deep learning, a plethora of NER methods based on deep learning have emerged. This paper begins by providing a succinct introduction to the definition of the problem and the limitations of traditional methods. It enumerates commonly used NER datasets suitable for deep learning methods and categorizes them into three classes based on the complexity of named entities. Then, some typical deep learning-based NER methods are summarized in detail according to the development history of deep learning models. Subsequently, an in-depth analysis and comparison of methods achieving outstanding performance on representative and widely used datasets is conducted. Furthermore, the paper reproduces and analyzes the recognition results of some typical models on three different types of typical datasets. Finally, the paper concludes by offering insights into the future trends of NER development. [ABSTRACT FROM AUTHOR]

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester. This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology. [ABSTRACT FROM AUTHOR]

Vision-language navigation (VLN) tasks require an agent to follow language instructions from a human guide to navigate in previously unseen environments using visual observations. This challenging field, involving problems in natural language processing (NLP), computer vision (CV), robotics, etc., has spawned many excellent works focusing on various VLN tasks. This paper provides a comprehensive survey and an insightful taxonomy of these tasks based on the different characteristics of language instructions. Depending on whether navigation instructions are given once or multiple times, we divide the tasks into two categories, i.e., single-turn and multiturn tasks. We subdivide single-turn tasks into goal-oriented and route-oriented tasks based on whether the instructions designate a single goal location or specify a sequence of multiple locations. We subdivide multiturn tasks into interactive and passive tasks based on whether the agent is allowed to ask questions. These tasks require different agent capabilities and entail various model designs. We identify the progress made on these tasks and examine the limitations of the existing VLN models and task settings. Hopefully, a well-designed taxonomy of the task family enables comparisons among different approaches across papers concerning the same tasks and clarifies the advances made in these tasks. Furthermore, we discuss several open issues in this field and some promising directions for future research, including the incorporation of knowledge into VLN models and transferring them to the real physical world. [ABSTRACT FROM AUTHOR]

Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

This paper delves into the intricate relationship between Artificial Intelligence (AI) and the art ecosystem, emphasizing the need for a decolonizing approach in the face of AI's growing influence. It argues that the development of AI is not just a technological leap but also a significant cultural and societal moment, akin to the advent of moving images that Walter Benjamin famously analyzed. The paper examines how AI, particularly in its current oligarchical and corporate-driven form, perpetuates and magnifies the existing social inequalities, thereby necessitating a critical and radical rethinking of its role in society and the arts. At the heart of the discussion is the concept of AI as a broad term encompassing various forms of machine intelligence, from natural language processing to computer vision. The paper criticizes the dominant anthropocentric view of intelligence and creativity, proposing a more inclusive approach that considers the diverse forms of intelligence present in other species and potentially in AI itself. It underscores the role of AI in shaping the art ecosystem, not just in the creative process but also in gatekeeping and decision-making. The paper proposes a framework for decolonizing AI in the art ecosystem, focusing on four key tasks: recognizing access as a form of power, understanding and addressing biases inherent in AI, assessing the impact of AI on marginalized communities, and challenging dominant narratives and epistemologies to create space for alternative voices and perspectives. It emphasizes the need for artists and the art community to engage actively with AI, shaping its development towards more equitable and just outcomes. In conclusion, the paper calls for a radical reimagination of AI's role in society and the arts, advocating for a future where AI is not just about technological advancement but also about fostering a more inclusive, equitable, and creatively diverse world. It invites artists, thinkers, and innovators to join in this journey of reimagining and reshaping the future of AI and the art ecosystem. [ABSTRACT FROM AUTHOR]

Information and communication technology has evolved dramatically, and now the majority of people are using internet and sharing their opinion more openly, which has led to the creation, collection and circulation of hate speech over multiple platforms. The anonymity and movability given by these social media platforms allow people to hide themselves behind a screen and spread the hate effortlessly. Online hate speech (OHS) recognition can play a vital role in stopping such activities and can thus restore the position of public platforms as the open marketplace of ideas. To study hate speech detection in social media, we surveyed the related available datasets on the web-based platform. We further analyzed approximately 200 research papers indexed in the different journals from 2010 to 2022. The papers were divided into various sections and approaches used in OHS detection, i.e., feature selection, traditional machine learning (ML) and deep learning (DL). Based on the selected 111 papers, we found that 44 articles used traditional ML and 35 used DL-based approaches. We concluded that most authors used SVM, Naive Bayes, Decision Tree in ML and CNN, LSTM in the DL approach. This survey contributes by providing a systematic approach to help researchers identify a new research direction in online hate speech. [ABSTRACT FROM AUTHOR]

Medical Image Captioning (MIC), is a developing area of artificial intelligence that combines two main research areas, computer vision and natural language processing. In order to support clinical workflows and decision-making, MIC is used in a variety of applications pertaining to diagnosis, therapy, report production, and computer-aided diagnosis. The generation of long and coherent reports highlighting correct abnormalities is a challenging task. Therefore, in this direction, this paper presents an efficient F D T - D r 2 T framework for the generation of coherent radiology reports with efficient exploitation of medical content. The proposed framework leverages the fusion of texture features and deep features in the first stage by incorporating ISCM-LBP + PCA-HOG feature extraction algorithm and Convolutional Triple Attention-based Efficient XceptionNet ( C - T a X N e t ). Further, fused features from the FDT module are utilized by the Dense Radiology Report Generation Transformer ( D r 2 T ) model with modified multi-head attention generating dense radiology reports by highlighting specific crucial abnormalities. To evaluate the performance of the proposed F D T - D r 2 T extensive experiments are conducted on publicly available IU Chest X-ray dataset and the best performance of the work is observed as 0.531 BLEU@1, 0.398 BLEU@2, 0.322 BLEU@3, 0.251 BLEU@4, 0.384 CIDEr, 0.506 ROUGE-L, 0.277 METEOR. An ablation study is carried out to support the experiments. Overall, the results obtained demonstrate the efficiency and efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. Scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP methods. We extend our previous work of the TinyGenius methodology in various ways. Specifically, we discuss the NLP tasks in more detail and include an explanation of the data model. Moreover, we present a user evaluation where participants validate the generated NLP statements. The results indicate that employing microtasks for statement validation is a promising approach despite the varying participant agreement for different microtasks. [ABSTRACT FROM AUTHOR]

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual information. With the rapid development of science and technology, a large number of textual reports have accumulated in the field of geology. However, many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining, making it more challenging for some researchers to extract necessary information from these texts. Natural Language Processing (NLP) has obvious advantages in processing large amounts of textual data. The objective of this paper is to identify geological named entities from Chinese geological texts using NLP techniques. We propose the Ro-BERTa-Prompt-Tuning-NER method, which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset configurations. The RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word vectors. Finally, we conducted experiments on the constructed Geological Named Entity Recognition (GNER) dataset. Our experimental results show that the proposed model achieves the highest F1 score of 80.64% among the four baseline algorithms, demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts. [ABSTRACT FROM AUTHOR]

Goal-oriented requirements engineering aims to describe both stakeholders and system goals and their relationships using goal models. Large goal models for complex systems are often constructed from sub-models describing various stakeholders' views and context-related aspects. These goal-oriented sub-models, also called views, may exhibit overlaps and present discrepancies. Hence, integrating such views is considered a significant barrier to the construction of a unified goal model. Current approaches to merging goal models require intensive human intervention. This paper proposes an approach and a prototype tool, called GRLMerger, to integrate two GRL (Goal-oriented Requirement Language) models into one consolidated model that is correct, complete, and free from any conflict that may arise during the merging process. GRLMerger considers both syntactic and semantic aspects of the GRL models and allows analysts to merge them either interactively or in a fully automated mode. GRLMerger employs natural language processing (NLP) techniques to match intentional elements based on their semantic similarity. The proposed GRLMerger approach and tool have been validated using 12 experimental tasks derived from two case studies, exhibiting very promising performance. [ABSTRACT FROM AUTHOR]

The heating of high-voltage isolating switches above 110 kV will cause serious safety accidents. To identify the heating faults of isolating switches accurately, this study uses natural language processing technology, knowledge graph (KG) technology and machine learning algorithm to mine the structured and unstructured mixed data. Firstly, based on bidirectional encoder representations (BERT) pre-training model, a bidirectional long short-term memory network and conditional random field model are introduced to identify entities related to high-voltage isolating switches and the thermal failures from the text data. Further, a convolutional neural network fusion attention mechanism model is leveraged to recognize the corresponding entity relationships from the text records. Then, the KGs of isolating switches and the heating faults are constructed, and a path ranking algorithm is proposed to deduce and identify the possible factors causing overheating on the KGs. Combined with the structured data, a support vector machine model based on focal loss function (FL-SVM) is proposed to recognize the heating faults of isolating switches. Experiments show that the performances of all six models used in this paper on the hybrid dataset are higher than those on the structured dataset. Such results suggest that joint text-structured data mining can compensate for insufficient structured data and thus, achieving a better heating failure recognition, especially for the infrequent malfunctions. Meanwhile, the proposed FL-SVM performs better than other models due to its excellent ability to solve the problem of imbalanced faults and normal samples. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) technology has made remarkable strides in the music industry, profoundly impacting various aspects of our lives. This progress has extended to revolutionizing industries, streamlining processes, and enhancing personalization in music. AI's expanding capabilities have driven computer vision, natural language processing, and machine learning breakthroughs. As music enthusiasts increasingly turn to online platforms, the need for effective recommendation systems has become more pressing. The exponential growth of musical content poses a challenge in discovering music tailored to individual tastes. This paper proposed a personalized music recommendation system powered by deep learning, explicitly utilizing convolutional neural networks (CNNs) to address the challenges posed by expanding music libraries and diverse song resources. In today's era of ever-growing music databases and increasing content diversity, more than traditional retrieval methods are needed to efficiently connect users with their preferred track. Traditional music platforms rely solely on retrieval mechanisms, necessitating manual inputs such as artist names and titles to locate specific songs. However, this approach needs in-depth music data analysis and delivers personalized recommendations generated by various deep learning-based algorithms. This paper bridges this gap by harnessing deep learning algorithms for comprehensive music data analysis and personalized song suggestions. The proposed CNN-based recommendation system explores the domains of deep learning and content-based approaches, empowering users to discover their favorite tunes swiftly. This innovative mechanism leverages CNNs and deep learning algorithms to offer efficient, personalized, and data-driven music recommendations, transforming the user experience with their music collections. In experimental results, the proposed CNN model is compared with various deep learning algorithms, including DNN, RNN, and LSTM, as well as traditional algorithms like SVM, KNN, RF, and DT. Impressively, the CNN model achieved the highest accuracy at 95%, surpassing all other algorithms. [ABSTRACT FROM AUTHOR]

Document similarity recognition is one of the most important problems in natural language processing. This paper proposes a plagiarism comparison mechanism called JCF. Initially, the TF–IDF scheme is applied to build a bag of words as the representation of the common features of all documents. Then, the plagiarism comparison is carried out in a coarse-grained manner, which speeds up the similarity comparison. Finally, the most similar documents can then be compared in detail based on a fine-grained approach. In addition, the JCF detects plagiarism at both syntax level and semantic-like level. To prevent the distortion of similarity comparison, this paper further develops a similarity restoration approach such that the proposed JCF can obtain both advantages of quickness and accuracy. Performance studies confirm that the proposed JCF outperforms existing studies in terms of precision, recall and F1 score. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users' expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data. [ABSTRACT FROM AUTHOR]

Recent advances in text generative models make it possible to create artificial texts that look like human-written texts. A large number of methods for detecting texts obtained using large language models have already been developed. However, improvement of detection methods occurs simultaneously with the improvement of generation methods. Therefore, it is necessary to explore new generative models and modernize existing approaches to their detection. In this paper, we present a large analysis of existing detection methods, as well as a study of lexical, syntactic, and stylistic features of the generated fragments. Taking into account the developments, we have tested the most qualitative, in our opinion, methods of detecting machine-generated documents for their further application in the scientific domain. Experiments were conducted for Russian and English languages on the collected datasets. The developed methods improved the detection quality to a value of 0.968 on the F1-score metric for Russian and 0.825 for English, respectively. The described techniques can be applied to detect generated fragments in scientific, research, and graduate papers. [ABSTRACT FROM AUTHOR]

Several recent efforts have been devoted to enhancing pretrained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and have achieved consistent improvements on various knowledge-driven natural language processing (NLP) tasks. However, most of these knowledge-enhanced PLMs, regardless of the domain-specific knowledge required by PLMs, lack a Chinese corpus to support large-scale PLMs, especially in domain-specific PLMs. Herein, we harvest and process public geological reports (mainly various reports from the National Geological Archives of China), abstracts of papers from 34 geological journals, and abstracts of geological dissertations from 161 universities or scientific institutions. This paper introduces a large-scale Chinese geological corpus (GeoCorpus) containing approximately 232 MB of training data with a total of 243 million characters. Based on GeoCorpus, we also introduce a Chinese pretrained language representation model in the geological domain (CnGeoPLM). The model utilizes the generic BERT to complete parameter initialization and combines the GeoCorpus for the second stage of training. We evaluate and test CnGeoPLM and Bidirectional Encoder Representations from Transformers (BERT) on three tasks of geological named entity recognition (GeoNER), geological entity relationship extraction (GeoRE), and geological entity clustering (GeoClu). In the results of both the GeoNER and GeoRE experiments, CnGeoPLM has different degrees of improvement in evaluation metrics compared to BERT. In the GeoClu experiment, CnGeoPLM is significantly more effective and discriminative in entity clustering effect compared to BERT. These results show that CnGeoPLM has a stronger representation ability in geological NLP tasks than BERT. [ABSTRACT FROM AUTHOR]

The emergence of Artificial Intelligence (AI) provides brand-new technical support and a broader resource platform for college oral English teaching and evaluation. The advancement of science, technology, and networks has resulted in significant educational changes. Because of these, the demand for English instruction is rising quickly, along with the level of cultural integration. However, due to the limits of conventional teaching techniques and the slow expansion of information technology, oral English classes have been discarded and discontinued for a long time. Based on the preceding, this paper first addresses the shortcomings of traditional college-level spoken English instruction by exposing content and timing deficiencies. It then describes how spoken English data are collected, preprocessed, and framed before being standardized for consistency. After that, this paper uses mapping recognition results using a DBN speech model and correlation coefficients in the evaluation. In addition, it uses the Pairwise Variability Index in rhythm evaluation to assess stress distribution differences between test and standard pronunciation. Besides the above, the proposed approach uses Support Vector Machine (SVM) optimization inside statistical learning to improve phoneme recognition and reliability, especially when dealing with difficult-to-distinguish phoneme sets. Further analysis is carried out using neural networks, which include excitation functions and error computations. Finally, the complete design emphasizes user-centric functional characteristics such as practicability, user needs, and robust system management subsystems. Experiments suggest that the natural language processing-based oral English teaching mode can increase students' overall oral English skills. These results reveal that students' excitement increased by 33.3%, their verbal fluency increased by 86%, and their vocabulary learning increased by 16.1%. The results show that this method can effectively assist students in learning oral English. [ABSTRACT FROM AUTHOR]

Question generation (QG) aims to generate natural questions from relevant input. Existing state-of-the-art QG approaches primarily leverage pre-trained language models (PLMs) to encode the deep semantics within the input. Meanwhile, studies show that the input's dependency parse tree (referred to as syntactic information) is promising in improving NLP-oriented tasks. However, how to incorporate syntactic information in PLMs to guide a QG process effectively still needs to be settled. This paper introduces a syntax-guided sentence-level QG model based on prompt learning. Specifically, we model the syntactic information by utilizing soft prompt learning, jointly considering the syntactic information from a constructed dependency parse graph and PLM to guide question generation. We conduct experiments on two benchmark datasets, SQuAD1.1 and MS MARCO. Experiment results show that our model exceeded both automatic and human evaluation metrics compared with mainstream approaches. Moreover, our case study shows that the model can generate more fluent questions with richer information. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising. [ABSTRACT FROM AUTHOR]

Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then such data need first to be preprocessed and vectorized (converted to numeric). Second, to overcome the curse of dimensionality caused by the vectorisation process, a dimension reduction is applied. A two-stage variable selection and feature extraction is used to reduce the high dimensionality of a feature space. We are first interested in studying the potential of using an unsupervised variable selection technique, the genetic algorithm, to identify the variables that best demonstrate discrimination in the separation and compactness of groups of textual data. The genetic algorithm uses a combination of the K-means or Gaussian Mixture Model clustering and validity indices as a fitness function for optimization. Such a function improves both compactness and class separation. The second work looks into the feasibility of applying a feature extraction technique. The adopted methodology is a Deep learning algorithm based on variational autoencoder (VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for cluster identification. The last objective of this paper is to propose a new methodology based on the combination between variational autoencoder (VAE) for the latent space disentanglement, and genetic algorithm (GA) to find, in an unsupervised way, the latent variables allowing the best discrimination of clusters of failure analysis data. This methodology is called VAE-GA. Experiments on textual datasets of failure analysis demonstrate the effectiveness of the VAE-GA proposed method which allows better discrimination of textual classes compared to the use of GA or VAE separately or the combination of PCA with GA (PCA-GA) or a simple Auto-encoders with GA (AE-GA). [ABSTRACT FROM AUTHOR]

Low-light image enhancement is a low-level vision task. Most of the existing methods are based on convolutional neural network(CNN). Transformer is a predominant deep learning model that has been widely adopted in various fields, such as natural language processing and computer vision. Compared with CNN, transformer has the ability to capture long-range dependencies to make full use of global contextual information. For low-light enhancement tasks, this capability can promote the model to learn the correct luminance, color and texture. We try to introduce transformer into the low-light image enhancement field. In this paper, we design a pyramid transformer with cross-shaped windows (CSwin-P). CSwin-P contains an encoder and decoder. Both the encoder and decoder contain several stages. Each stage contains several enhanced CSwin transformer blocks (ECTB). ECTB uses cross-shaped window self-attention and a feed-forward layer with spatial interaction unit. Spatial interaction unit can further capture local contextual information through gating mechanism. CSwin-P uses implicit positional encoding, and the model is unrestricted by the image size in the inference phase. Numerous experiments prove that our method is superior to the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The article discusses the concept of Artificial General Intelligence (AGI) and its distinction from Artificial Narrow Intelligence (ANI). AGI refers to machines that can perform any intellectual task that a human can, while ANI is limited to specific programmed tasks. The article provides a brief history of AI research and the efforts made to achieve AGI. It also highlights the limitations of current AI systems and the need for a hybrid approach that combines symbolic AI and statistical AI. The article concludes by mentioning the papers included in a special issue on neuro-symbolic AI, covering topics such as depression detection, ontology matching, explainable AI, sentiment analysis, text classification, legal judgment analysis, and thunderstorm occurrence categorization. [Extracted from the article]

These days, the world has become a non-denominational place due to cyberspace. Digital services such as search portals and social platforms have become intricate with the regular course of daily life. As a massive number of clients' thoughts are available on the internet, sentiment analysis has become one of the most prolific investigation arenas in natural language processing. Sarcasm detection is a vital phase in sentiment analysis because of the intrinsically vague nature of sarcasm. To subsidize as a resolution to the current ever-increasing arena of attention on the ever-increasing volume of digital data, this work proposes a novel voting-based ensemble approach for sarcasm detection utilizing deep learning techniques. This paper uses Bidirectional Encoder Representations from Transformers to build contextual word embeddings and feeds those to the network, which is an ensemble of four deep learning models. Convolutional Neural Network, Bidirectional Long Short-Term Memory, and parallel and sequential combinations of both are among the models. For classification, the acquired features from each model are experimented with four machine learning classifiers, such as Support Vector Machine, Least Squares Support Vector Machine, Multinomial Naive Bayes, and Random Forest, along with Sigmoid activation function. Out of these five, the optimal classifier is selected for each model. Finally, a majority voting is performed on the outputs of all four models to differentiate between sarcastic and non-sarcastic texts. The proposed approach has been assessed using two benchmark datasets containing English-language texts, namely, news headlines and the self-annotated reddit corpus. Experiments demonstrated that the proposed methodology produces an accuracy of 94.89% on the news headlines repository, which is a gain of 2.99%, and an F1-score of 80.49% on the self-annotated reddit corpus, which is an improvement of 0.52% over the previous state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

The paper presents a novel data augmentation-based approach to develop explainable, deep learning models for hate speech detection. Hate speech is widely prevalent on online social media but difficult to detect automatically due to challenges of natural language processing and complexity of hate speech. Further, the decisions of the existing solutions possess constrained explainability since limited annotated data are available for training and testing of models. Therefore, this work proposes the use of text-based data augmentation for improving the performance and explainability of deep learning models. Techniques based on easy data augmentation, bidirectional encoder representations from transformers and back translation have been utilized for data augmentation. Convolutional neural networks and long short-term memory models are trained with augmented data and evaluated on two publicly available datasets for hate speech detection. Methods of LIME and integrated gradients are used to retrieve explanations of the deep learning models. A diagnostic study is conducted on test samples to check for improvement in the models as a result of the data augmentation. The experimental results verify that the proposed approach improves the explainability as well as the accuracy of hate speech detection. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is a key step for many natural language processing tasks such as information search, automatic translation, and sentiment analysis. WSD is the process that identifies appropriate senses of ambiguous words in the text. With the increasing number of words to be disambiguated in large amount of text data, WSD becomes very challenging and that is why an exhaustive search for the best set of senses may be unpractical. Recently, several metaheuristic approaches have been proposed for different complex optimization problems and have achieved good results. Therefore, in order to improve the WSD process, in this paper, the WSD problem is modeled as a combinatorial optimization problem, and the Discrete Student Psychology-Based Optimization (DSPBO) metaheuristic is proposed and used to selecting appropriate senses. A DSPBO-based WSD is proposed to disambiguate more ambiguous words together in function to their contexts in the target text, and a Lesk-based fitness function is used to guide the DSPBO metaheuristic to optimize the general semantic similarity of selected senses. The proposed approach is evaluated and compared to several recent WSD approaches on the well-known corpuses SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15. The comparison is made in terms of F-Measure, precision, and recall. Experiments show a significant improvement both over existing knowledge lexicon-based approaches and metaheuristic-based approaches, with a higher F-measure of 84.21%, 83.33%, 87.5%, 77.58%, and 81.08% on SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15, respectively. [ABSTRACT FROM AUTHOR]

Business process compliance is an essential part of business process management, which saves organizations from penalties caused by non-compliant processes. However, current researches on business process compliance mainly focus on checking using general constraint rules that have been formalized without in-depth analysis of related regulatory documents and mostly involve extensive human efforts. In this paper, we aim to propose an automatic and interpretable compliance checking approach for design-time business processes. By combining deep learning and a natural language processing approach based on rule templates, we extract semantic information from regulatory documents at different granularities for subsequent compliance checking. In addition, we match appropriate rules to the process model and detect the degree of violation of the business process from three control-flow perspectives. The effectiveness of this method is validated on two real-world datasets. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is a study area focused on obtaining contextual polarity from the text. Currently, deep learning has obtained outstanding results in this task. However, much annotated data are necessary to train these algorithms, and obtaining this data is expensive and difficult. In the context of low-resource scenarios, this problem is even more significant because there are little available data. Transfer learning (TL) can be used to minimize this problem because it is possible to develop some architectures using fewer data. Language models are a way of applying TL in natural language processing (NLP), and they have achieved competitive results. Nevertheless, some models need many hours of training using many computational resources, and in some contexts, people and organizations do not have the resources to do this. In this paper, we explore the models BERT (Pretraining of Deep Bidirectional Transformers for Language Understanding), MultiFiT (Efficient Multilingual Language Model Fine-tuning), ALBERT (A Lite BERT for Self-supervised Learning of Language Representations), and RoBERTa (A Robustly Optimized BERT Pretraining Approach). In all of our experiments, these models obtain better results than CNN (convolutional neural network) and LSTM (Long Short Term Memory) models. To MultiFiT and RoBERTa models, we propose a pretrained language model (PTLM) using Twitter data. Using this approach, we obtained competitive results compared with the models trained in formal language datasets. The main goal is to show the impacts of TL and language models comparing results with other techniques and showing the computational costs of using these approaches. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal "Language Resources & Evaluation" focused on computational approaches to the Portuguese language. The Portuguese language is spoken by approximately 250 million people and is the official language in 10 regions around the world. The special issue aims to provide an overview of the computational processing of Portuguese over the past three decades and showcase the theoretical contributions, resources, tools, and applications that have been developed. The issue includes papers on various topics such as language modeling, corpus construction, sentiment analysis, and creative text generation. The authors hope that this special issue will inspire new research initiatives and international collaboration in the field of Portuguese language processing. [Extracted from the article]

Much recent effort has been devoted to creating large-scale language models. Nowadays, the most prominent approaches are based on deep neural networks, such as BERT. However, they lack transparency and interpretability, and are often seen as black boxes. This affects not only their applicability in downstream tasks but also the comparability of different architectures or even of the same model trained using different corpora or hyperparameters. In this paper, we propose a set of intrinsic evaluation tasks that inspect the linguistic information encoded in models developed for Brazilian Portuguese. These tasks are designed to evaluate how different language models generalise information related to grammatical structures and multiword expressions (MWEs), thus allowing for an assessment of whether the model has learned different linguistic phenomena. The dataset that was developed for these tasks is composed of a series of sentences with a single masked word and a cue phrase that helps in narrowing down the context. This dataset is divided into MWEs and grammatical structures, and the latter is subdivided into 6 tasks: impersonal verbs, subject agreement, verb agreement, nominal agreement, passive and connectors. The subset for MWEs was used to test BERTimbau Large, BERTimbau Base and mBERT. For the grammatical structures, we used only BERTimbau Large, because it yielded the best results in the MWE task. In both cases, we evaluated the results considering the best candidates and the top ten candidates. The evaluation was done both automatically (for MWEs) and manually (for grammatical structures). The results obtained for MWEs show that BERTimbau Large surpassed both the other models in predicting the correct masked element. However, the average accuracy of the best model was only 52% when only the best candidates were considered for each sentence, going up to 66% when the top ten candidates were taken into account. As for the grammatical tasks, the results presented better prediction, but also varied depending on the type of morphosyntactic agreement. On the one hand, cases such as connectors and impersonal verbs, which do not require any agreement in the produced candidates, had precision of 100% and 98.78% among the best candidates. On the other hand, tasks that require morphosyntactic agreement had results consistently below 90% overall precision, with the lowest scores being reported for nominal agreement and verb agreement, both having scores below 80% in overall precision among the best candidates. Therefore, we identified that a critical and widely adopted resource for Brazilian Portuguese NLP presents issues concerning MWE vocabulary and morphosyntactic agreement, even if it is prolific in most cases. These models are a core component in many NLP systems, and our findings demonstrate the need of additional improvements in these models and the importance of widely evaluating computational representations of language. [ABSTRACT FROM AUTHOR]

This paper introduces an effective method for improving dependency parsing which is based on a graph embedding model. The model helps extract local and global connectivity patterns between tokens. This method allows neural network models to perform better on dependency parsing benchmarks. We propose to incorporate node embeddings trained by a graph embedding algorithm into a bidirectional recurrent neural network scheme. The new model outperforms a baseline reference using a state-of-the-art method on three dependency treebanks for both low-resource and high-resource natural languages, namely Indonesian, Vietnamese and English. We also show that the popular pretraining technique of BERT would not pick up on the same kind of signal as graph embeddings. The new parser together with all trained models is made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide in total. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

Span-level method achieves competitive results in Aspect Sentiment Triplet Extraction (ASTE) by enumerating all possible spans. However, previous span-level methods fail to exploit syntactic information to identify the correspondence between aspect terms and opinion terms, which makes the extracted triplets inaccurate. In this paper, we propose a syntactic and semantic dual-enhanced bidirectional network (SSBN) for ASTE task. By constructing word dependencies as a graph and embedding them into features to capture syntactic information more effectively in bidirectional network. Furthermore, we design a pruning strategy that uses part-of-speech information to alleviate the problem of identifying potential aspects and opinions from a large number of spans. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the effectiveness of the SSBN model. [ABSTRACT FROM AUTHOR]

Within any scientific disciplines, a large amount of data are buried within various literature depositories and archives, making it difficult to manually extract useful information from the datum swamps. The machine-learning extraction of data therefore is necessary for the big-data-based studies. Here, we develop a new text-mining technique to reconstruct the global database of the Precambrian to Recent stromatolites, providing better understanding of secular changes of stromatolites though geological time. The step-by-step data extraction process is described as below. First, the PDF documents of stromatolite-containing literatures were collected, and converted into text formation. Second, a glossary and tag-labeling system using NLP (Natural Language Processing) software was employed to search for all possible candidate pairs from each sentence within the papers collected here. Third, each candidate pair and features were represented as a factor graph model using a series of heuristic procedures to score the weights of each pair feature. Occurrence data of stromatolites versus stratigraphical units (abbreviated as Strata), facies types, locations, and age worldwide were extracted from literatures, respectively, and their extraction accuracies are 92%/464, 87%/778, 92%/846, and 93%/405 from 3 750 scientific abstracts, respectively, and are 90%/1 734, 86%/2 869, 90%/2 055 and 91%/857 from 11 932 papers, respectively. A total of 10 072 unique datum items were identified. The newly obtained stromatolite dataset demonstrates that their stratigraphical occurrences reached a pronounced peak during the Proterozoic (2 500–541 Ma), followed by a distinct fall during the Early Phanerozoic, and overall fluctuations through the Phanerozoic (541–0 Ma). Globally, seven stromatolite hotspots were identified from the new dataset, including western United States, eastern United States, western Europe, India, South Africa, northern China, and southern China. The proportional occurrences of inland aquatic stromatolites remain rather low (∼ 20%) in comparison to marine stromatolites from the Precambrian to Jurassic, and then display a significant increase (30%–70%) from the Cretaceous to the present. [ABSTRACT FROM AUTHOR]

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives. [ABSTRACT FROM AUTHOR]

Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing. [ABSTRACT FROM AUTHOR]

For this special issue of I New General Computing i , we have selected two papers that received particularly excellent evaluations from among the oral presentations at the international sessions of the 35th Japanese Society for Artificial Intelligence (JSAI2021) held from June 8 (Tuesday) to June 11 (Friday), 2021. The two papers were reviewed by four NGC Guest Editors and NGC Guest Editorial Members, and it was determined that both papers met the criteria to be recommended to NGC. We asked each International Program Committee member to recommend one paper to the New Generation Computing (NGC) Program Committee if it was particularly outstanding. [Extracted from the article]

There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis. [ABSTRACT FROM AUTHOR]

The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns. In summary, in this paper, we present an automated data efficient approach based on weak supervision and deep learning (BERT + WS) for identification of news articles about economical uncertainty and adapt the calculation of EPU to the proposed strategy. Experimental results reveal that our approach (BERT + WS) improves over the baseline method centered in keyword search, which is currently used to construct the EPU index. The improvement is over 20 points in precision, reducing the false positive rate typical to the use of keywords. [ABSTRACT FROM AUTHOR]

In order to enable students to directly face empirical data, summarize translation rules and learn translation skills, this paper studies the basis, motivation and methods of applying research dynamics in translation and teaching. Presenting data in class is the main method of dynamically searching corpora, which enables learners to face enough bilingual data that are easy to choose, and makes translation skills and teaching of translation of selected language items relatively focused. In recent years, the emotional analysis text has attracted academic scientists, and the professionals involved in the research, the use of research methods, and the cultural background related to language have become more and more extensive. In this paper, natural language processing is used to analyze emotions contained in translated texts. Natural language processing not only helps to manage the huge ability of data to efficiently translate text, but also helps to extract the hidden emotions in text translation. It only takes half the effort to achieve the multiplier effect. The multi label classification in natural language processing can reflect the information contained in emotion. The translated text is more detailed, which is helpful for further research. [ABSTRACT FROM AUTHOR]

Whether it is an NLP (natural language processing) task or an NLU (natural language understanding) task, many methods are model oriented, ignoring the importance of data features. Such models did not perform well for many tasks based on feature loose, unbalanced tricky data including text classification tasks. In this regard, this paper proposes a classification method called LSTM-SN (long-short term memory RNN fusion social network) based on extremely complex datasets. The approach condenses the characteristics of the dataset. LSTM combines with social network methods derived from specific datasets to complete the classification task, and then use complex network structure evolution methods to discover dynamic social attributes. The experimental results show that this method can overcome the shortcomings of traditional methods and achieve better classification results. Finally, a method to calculate the accuracy of fusion model is proposed. The research ideas of this paper have far-reaching significance in the domain of social data analysis and relation extraction. [ABSTRACT FROM AUTHOR]