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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]

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X. [ABSTRACT FROM AUTHOR]

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]

Purpose: This study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system. Design/methodology/approach: A semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks. Findings: The relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems. Originality/value: This is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283. [ABSTRACT FROM AUTHOR]

Zero-Shot Learning (ZSL) aims to classify images of new categories in the testing phase without labeled images during training, using examples from categories with labeled images and some auxiliary information. The auxiliary information includes semantic attributes, textual descriptions, word embeddings, etc., for both labeled and unlabeled classes, utilizing Natural Language Processing (NLP) approaches. The word embeddings created are limited by the semantic attributes and textual descriptions where the semantics of categories are insufficient. In this paper, introduces a study for Generalized Zero-Shot Learning (GZSL), a type of ZSL, by integrating the rich semantics offered by ontology. Semantic attributes used for semantic representation are supported by ontology. Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) network architectures are used together to synthesize visual features. Our work was evaluated on the AWA2 dataset, and improvement in GZSL performance was achieved. [ABSTRACT FROM AUTHOR]

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. [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]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science's diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as "inclusive society" and "early childhood development", by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs. [ABSTRACT FROM AUTHOR]

Many scientific texts are produced in Persian and available in scientific information databases through the Web. In this paper, FarsAcademic, a test collection of Persian scientific texts, has been built to implement information retrieval models among academic search comprising 102238 documents and 61 topics. While constructing FarsAcademic, we have tried to resolve the problems specific to information retrieval (IR) and natural language processing (NLP) in Persian scientific texts. Domain experts were employed to create queries within their research area, and user relevance and topical relevance were applied to improve the precision of relevance judgment of documents. Further, to improve retrieval performance in Persian scientific texts, automated query expansion was applied using one of the relevant feedback techniques, the Local Context Analysis algorithm. The result showed that query expansion techniques outperformed other information retrieval models in the Persian scientific texts retrieval task. Eventually, FarsAcademic became the only one that has been free of charge for Iranian information retrieval scholars to implement and evaluate different information retrieval models and algorithms on Persian scientific text and academic search. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D's based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D's when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not. [ABSTRACT FROM AUTHOR]

Information retrieval is an important field that aims to provide a relevant document to a user information need, expressed through a query. Arabic is a challenging language that gained much attention recently in the information retrieval domain. To overcome the problems related to its complexity, many studies and techniques have been presented, most of them were conducted to solve the stemming problem. This paper presents an overview of the Arabic information retrieval process, including various text processing techniques, ranking approaches, evaluation measures, and some important information retrieval models. The paper finally presents some recent related studies and approaches in different Arabic information retrieval fields. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications rely on pre-trained text representations, leading to the development of a number of neural network language models for various languages. However, this is not the case for Amharic, which is known to be a morphologically complex and under-resourced language. Usable pre-trained models for automatic Amharic text processing are not available. This paper presents an investigation on the essence of learned text representation for information retrieval and NLP tasks using word embeddings and BERT language models. We explored the most commonly used methods for word embeddings, including word2vec, GloVe, and fastText, as well as the BERT model. We investigated the performance of query expansion using word embeddings. We also analyzed the use of a pre-trained Amharic BERT model for masked language modeling, next sentence prediction, and text classification tasks. Amharic ad hoc information retrieval test collections that contain word-based, stem-based, and root-based text representations were used for evaluation. We conducted a detailed empirical analysis on the usability of word embeddings and BERT models on word-based, stem-based, and root-based corpora. Experimental results show that word-based query expansion and language modeling perform better than stem-based and root-based text representations, and fastText outperforms other word embeddings on word-based corpus. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-granularity information association approaches are used to uncover multi-level geological knowledge and support accurate retrieval of mineral resource geological reports. • Extracting geological multiple feature information (topic, time, space, figure, and table) based on the multi-granularity of geological reports. • Mining potential multiple feature information associations by an improved apriori algorithm. Massive geologic report contains all kinds of multimodal geologic data information (geologic text, geologic maps, geologic tables, etc.), which contain a lot of rich geologic basic knowledge and expert experience knowledge about rocks and minerals, stratigraphic structure, geologic age, geographic location, and so on. Accurate retrieval of specific information from massive geologic data has become an important need for geologic information retrieval. However, the majority of existing research primarily revolves around extracting and associating information at a single granularity to facilitate geological semantic retrieval, which ignores many potential semantic associations, leading to ambiguity and fuzziness in semantic retrieval. To solve these problems, this paper proposes a multi-granularity (document-chapter-paragraph) geological information retrieval framework for accurate semantic retrieval. The framework firstly extracts topic feature information, spatiotemporal feature information, figure and table feature information based on the multi-granularity of geological reports. Then, an improved apriori algorithm is used to mine and visualize the associations among the feature information to discover the semantic associations of the geological reports at multiple levels of granularity. Finally, experiments are designed to validate the application of the proposed multi-granularity information retrieval framework on the accurate retrieval of geological reports. The experimental results show that the proposed multi-granularity information retrieval framework in this paper can dig deeper into underlying geo-semantic information and realize accurate retrieval. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields. [ABSTRACT FROM AUTHOR]

News feeds generate colossal amount of data consisting of important information hidden in the intricacies. State of the art methods are still at infancy in providing a very generic and publicly available solution to skim through the important information in the news from various sources and an ability to search using specific keywords in different languages. This paper focuses on designing a tool to extract semantic details from news articles published through various internet sources in various languages. The semantic information is stored within DBMS for ease of organizing and retrieving the data. Further, a querying facility to search through entire articles based on the keyword or date-based search is also proposed to view the crisp content. The news articles in English, and two Indian languages - Hindi and Malayalam are considered for experimentation. The proposed strategy consists of two main components namely, Generative model creation and Query engine. Generative model aims to extract important entities and keywords along with their relevance to the article and other similar articles using Latent Dirichlet Allocation(LDA) and Named Entity Recognition(NER). Query engine is to facilitate on the fly retrieval of semantic content from the database, based on user keyword. The search engine, along with database indexing, reduces the access time to the database thereby retrieving the information in less time. Experimental results show that the proposed method is effective in terms of quality of information and time consumed for information retrieval. [ABSTRACT FROM AUTHOR]

Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a vector given a single sentence and are used in tasks such as textual similarity or information retrieval due to their low computational cost; however, their performance is inferior to that of cross-encoders. In this paper, we present Sentence-CROBI, an architecture that combines cross-encoders and bi-encoders to obtain a global representation of sentence pairs. We evaluated the proposed architecture in the paraphrase identification task using the Microsoft Research Paraphrase Corpus, the Quora Question Pairs dataset, and the PAWS-Wiki dataset. Our model obtains competitive results compared with the state-of-the-art by using model ensembles and a simple model configuration. These results demonstrate that a simple architecture that combines sentence pair and single-sentence representations without using complex pre-training or fine-tuning algorithms is a viable alternative for sentence pair tasks. [ABSTRACT FROM AUTHOR]

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set. [ABSTRACT FROM AUTHOR]

More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature. [ABSTRACT FROM AUTHOR]

Efficient information search from building information models (BIMs) requires deep BIM knowledge or extensive engineering efforts for building natural language (NL)-based interfaces. To address this challenge, this paper introduces a dynamic prompt-based virtual assistant framework dubbed "BIMS-GPT" that integrates generative pre-trained transformer (GPT) technologies, supporting NL-based BIM search. To understand users' NL queries, extract relevant information from BIM databases, and deliver NL responses along with 3D visualizations, a dynamic prompt-based process was developed. In a case study, BIMS-GPT's functionality is demonstrated through a virtual assistant prototype for a hospital building. When evaluated with a BIM query dataset, the approach achieves accuracy rates of 99.5% for classifying NL queries with incorporating 2% of the data in prompts. This paper contributes to the advancement of effective and versatile virtual assistants for BIMs in the construction industry as it significantly enhances BIM accessibility while reducing the engineering and training data prerequisites for processing NL queries. • Introduced a dynamic prompt-based virtual assistant for BIM information search • Integrated BIM and GPT technologies for developing natural language interfaces • Explored prompt engineering for GPT to interpret NL queries and summarize NL answers • Improved BIM accessibility by enabling NL-based interactions with 3D visualizations • Reduced engineering and training data prerequisites for processing BIM NL queries [ABSTRACT FROM AUTHOR]

Background. The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years. Methods. We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results. We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions. Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration. [ABSTRACT FROM AUTHOR]

Conversational software, or chatbots in popular parlance, have been considered a potential game changer for people-centric institutions for some time now. In sync with the trend, various libraries and knowledge resource centres have also adopted chatbots within their technological fold, with an aim to provide improved services to patrons. The only bottleneck towards such implementation has been the dearth of open source conversational software platforms. The purpose of the paper is to conceptualise a novel library chatbot using a recently developed, artificial intelligence-powered open source conversational software platform named Rasa, and to propose its potential adoption by libraries. The paper introduces the essence of chatbot technology and their present-day application in libraries. The paper also illustrates the technical underpinnings of Rasa Stack that can be leveraged to develop a library chatbot, and reflects on the potential future research in this direction. [ABSTRACT FROM AUTHOR]

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream. [ABSTRACT FROM AUTHOR]

Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively. [ABSTRACT FROM AUTHOR]

The recent development of deep learning-based natural language processing (NLP) methods has fostered many downstream applications in various fields. As one of the applications in the financial industry, fine-grained financial sentiment analysis (FSA) aims to understand the sentimental orientation, i.e., bullish or bearish, of financial texts by predicting the polarity score and has been widely applied in the financial industry stock-related opinion mining. Because of the lack of a large-scale labeled dataset and the domain-dependent nature, FSA is challenging. Previous works mainly focus on constructing and exploiting handcrafted lexicons that encode expert knowledge to enhance the semantic features in decision making, which yields improvements but are expensive to acquire. This paper proposes a lightweight regression model incorporating the statistical distribution of a term over the polarity range, say between − 1 and 1, to address the fine-grained FSA task. More concretely, we first count each word's appearance at different polarity intervals and produce a statistic-based representation for each text, which will be encoded as a corpus-level statistical feature vector by an autoencoder. Subsequently, the obtained feature vector will be integrated with the semantic feature vector in the regression model. Our experiments show such a model can produce significant improvements compared with the baseline models on two FSA subsets, i.e., news headlines and microblogs, without a computational overhead. Furthermore, we notice the signs that lexicon-based approaches have neglected can play an important role in FSA. [ABSTRACT FROM AUTHOR]

Many tasks of natural language processing such as information retrieval, intelligent question answering, and machine translation require the calculation of sentence similarity. The traditional calculation methods used in the past could not solve semantic understanding problems well. First, the model structure based on Siamese lack of interaction between sentences; second, it has matching problem which contains lacking position information and only using partial matching factor based on the matching model. In this paper, a combination of word and word's dependence is proposed to calculate the sentence similarity. This combination can extract the word features and word's dependency features. To extract more matching features, a bi-directional multi-interaction matching sequence model is proposed by using word2vec and dependency2vec. This model obtains matching features by convolving and pooling the word-granularity (word vector, dependency vector) interaction sequences in two directions. Next, the model aggregates the bi-direction matching features. The paper evaluates the model on two tasks: paraphrase identification and natural language inference. The experimental results show that the combination of word and word's dependence can enhance the ability of extracting matching features between two sentences. The results also show that the model with dependency can achieve higher accuracy than these models without using dependency. [ABSTRACT FROM AUTHOR]

A rapid growth has occurred for the act of plagiarism with the aid of Internet explosive growth wherein a massive volume of information offered with effortless use and access makes plagiarism the process of taking someone else’s work (represented by ideas, or even words) and representing it as other's own work easy to be performed. For ensuring originality, detecting plagiarism has been massively necessitated in various areas so that the people who aim to plagiarize ought to offer considerable effort for introducing works centered on their research. In this paper, work has been proposed for improving the detection of textual plagiarism through proposing a model for candidate retrieval phase. The model proposed for retrieving candidates has adopted the vector space method VSM as a retrieval model and centered on representing documents as vectors consisting of average term weights and considering them as queries for retrieval instead of representing them as vectors of term weight. The detailed comparison task comes as the second phase wherein fuzzy semantic based string similarity has been applied. Experiments have been conducted using PAN-PC-10 as an evaluation dataset for evaluating the proposed system. As the problem statement in this paper is restricted to detect extrinsic plagiarism and works on English documents, experiments have been performed on the portion dedicated to extrinsic detection and on documents in English language only. For evaluating performance of the proposed model for retrieving candidates, Precision, Recall, and F-measure have been used as an evaluation metrics. The overall performance of the proposed system has been assessed through the use of the five standard PAN measures Precision, Recall, F-measure, Granularity and . The experimental results have clarified that the proposed model for retrieving candidates has a positive impact on the overall performance of the system and the system outperforms the other state-of-the-art methods. They clarified that the proposed model has detected about 80% of the plagiarism cases and about 90% of the detections were correct. The proposed model has the ability to detect literal plagiarism in addition to cases containing paraphrasing. Performance comparison has clarified that the proposed system is either comparable or outperforms the other baseline systems in terms of the five evaluation metrics. [ABSTRACT FROM AUTHOR]

Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval. In order to improve disambiguation accuracy, this paper proposes a WSD method based on the graph convolutional network (GCN). Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. Word2Vec tool, Doc2Vec tool, pointwise mutual information (PMI), and TF-IDF are applied to compute embeddings of nodes and edge weights. GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. Training corpus of SemEval-2007: Task #5 is adopted to optimize the proposed WSD classifier. Test corpus of SemEval-2007: Task #5 is used to test the performance of WSD classifier. Experimental results show that average accuracy of the proposed method is improved. [ABSTRACT FROM AUTHOR]

Background: Text Matching (TM) is a fundamental task of natural language processing widely used in many application systems such as information retrieval, automatic question answering, machine translation, dialogue system, reading comprehension, etc. In recent years, a large number of deep learning neural networks have been applied to TM, and have refreshed benchmarks of TM repeatedly. Among the deep learning neural networks, convolutional neural network (CNN) is one of the most popular networks, which suffers from difficulties in dealing with small samples and keeping relative structures of features. In this paper, we propose a novel deep learning architecture based on capsule network for TM, called CapsTM, where capsule network is a new type of neural network architecture proposed to address some of the short comings of CNN and shows great potential in many tasks.Methods: CapsTM is a five-layer neural network, including an input layer, a representation layer, an aggregation layer, a capsule layer and a prediction layer. In CapsTM, two pieces of text are first individually converted into sequences of embeddings and are further transformed by a highway network in the input layer. Then, Bidirectional Long Short-Term Memory (BiLSTM) is used to represent each piece of text and attention-based interaction matrix is used to represent interactive information of the two pieces of text in the representation layer. Subsequently, the two kinds of representations are fused together by BiLSTM in the aggregation layer, and are further represented with capsules (vectors) in the capsule layer. Finally, the prediction layer is a connected network used for classification. CapsTM is an extension of ESIM by adding a capsule layer before the prediction layer.Results: We construct a corpus of Chinese medical question matching, which contains 36,360 question pairs. This corpus is randomly split into three parts: a training set of 32,360 question pairs, a development set of 2000 question pairs and a test set of 2000 question pairs. On this corpus, we conduct a series of experiments to evaluate the proposed CapsTM and compare it with other state-of-the-art methods. CapsTM achieves the highest F-score of 0.8666.Conclusion: The experimental results demonstrate that CapsTM is effective for Chinese medical question matching and outperforms other state-of-the-art methods for comparison. [ABSTRACT FROM AUTHOR]

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity. [ABSTRACT FROM AUTHOR]

Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query‐independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek‐Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state‐of‐the‐art baseline models, the chronological term rank model and the Markov random field model. [ABSTRACT FROM AUTHOR]

Scientific literature is currently the most important resource for scholars, and their citations have provided researchers with a powerful latent way to analyze scientific trends, influences and relationships of works and authors. This paper is focused on automatic citation analysis and summarization for the scientific literature of computational linguistics, which are also the shared tasks in the 2016 workshop of the 2nd Computational Linguistics Scientific Document Summarization at BIRNDL 2016 (The Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries). Each citation linkage between a citation and the spans of text in the reference paper is recognized according to their content similarities via various computational methods. Then the cited text span is classified to five pre-defined facets, i.e., Hypothesis, Implication, Aim, Results and Method, based on various features of lexicons and rules via Support Vector Machine and Voting Method. Finally, a summary of the reference paper from the cited text spans is generated within 250 words. hLDA (hierarchical Latent Dirichlet Allocation) topic model is adopted for content modeling, which provides knowledge about sentence clustering (subtopic) and word distributions (abstractiveness) for summarization. We combine hLDA knowledge with several other classical features using different weights and proportions to evaluate the sentences in the reference paper. Our systems have been ranked top one and top two according to the evaluation results published by BIRNDL 2016, which has verified the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model based on the main sections of a scientific document and a collection of citing sentences (citances) from papers that reference the document. The model was adapted from earlier work done on Biomedical documents used in the summarization task of the 2014 Text Analysis Conference (TAC 2014). The mixture model trained on the Biomedical data was used also on the data for the Computational Linguistics scientific summarization task of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (CL-SciSumm 2016). This model gives rise to machine-generated summaries with ROUGE scores that are nearly as strong as those seen on the Biomedical data and was also the highest scoring submission to the task of generating a human summary. For sentence extraction, we use the OCCAMS algorithm (Davis et al., in: Vreeken, Ling, Zaki, Siebes, Yu, Goethals, Webb, Wu (eds) ICDM workshops, IEEE Computer Society, pp 454-463, 2012) which takes the sentences from the original document and the assignment of weights of the terms computed by the language models and outputs a set of minimally overlapping sentences whose combined term coverage is maximized. Finally, we explore the importance of an appropriate background model for the hypothesis test to select terms to achieve the best quality summaries. [ABSTRACT FROM AUTHOR]

Automatic text summarization is considered as an important task in various fields in natural language processing such as information retrieval. It is a process of automatically generating a text representation. Text summarization can be a solution to the problem of information overload. Hence, with the large amount of information available on the Internet, the presentation of a document by a summary helps to get the most relevant result of a search. We propose in this paper a new free Arabic structured corpus in the standard XML TREC format. ANT corpus v2.1 is collected using RSS feeds from different news sources. This corpus is useful for multiple text mining purposes such as generic text summarization, clustering or classification. We test this corpus for an unsupervised single-document extractive summarization using statistical and graph-based language-independent summarizers such as LexRank, TextRank, Luhn and LSA. We investigate the sensitivity of the summarization process to the stemming and stop words removal steps. We evaluate these summarizers performance by comparing the extracted texts fragments to the abstracts existing in ANT corpus v2.1 using ROUGE and BLEU metrics. Experimental results show that LexRank summarizer has achieved the best scores for the ROUGE metric using the stop words removal scenario. [ABSTRACT FROM AUTHOR]

Automatic themes-based classification of Quran verses is the process of classifying verses to predefined categorizes or themes. It is an essential task for all Muslims and people interested in studying the Quran. Quran themes-based classification could be used in many natural language processing (NLP) fields such as search engines, data mining, question–answering systems, and information retrieval applications. This paper presents an ensemble multi-label classification model that automatically identifies and classifies the Quran verses based on themes/topics. The model is composed of four phases: pre-processing, data vectorization, binary relevance classifier, and voting module. Firstly, the verses of the second chapter of the Quran (Al-Baqarah) are tokenized and normalized. Then, the topics of these verses are manually labeled based on "Mushaf Al-Tajweed" classification. Secondly, verses are converted into features' vectors using term frequency-inverse document frequency (TF-IDF) and word2vec techniques. Word2vec is used to consider the semantic meaning of Quranic words and to improve performance. Also, they are trained on a collected classic Arabic corpus of 200 million words. Then, the binary relevance multi-label classification technique is applied using three different classifiers: logistic regression, support vector machine, and random forest, which categorize verses into 393 topics/tags. Finally, the voting module is applied by picking the tags with the maximum prediction probability among the three classifiers. The results of the three classifiers and the ensemble model are compared against "Mushaf Al-Tajweed." The ensemble model outperforms the three classifiers. Its average hamming loss, recall, precision, and F1-Score are 0.224, 81%, 75%, and 77%, respectively. [ABSTRACT FROM AUTHOR]

Aligned multilingual corpora have become essential resources in multilingual Natural Language Processing (NLP) in the last decades, as well as one of the major resources for researchers in various areas of linguistics and related language disciplines. Parallel corpora are language corpora that contain a collection of one or more original texts in one language and their translations into one or more other languages. Original texts and their translations are aligned at some level of text divisions (e.g. sentence, paragraph, and chapter level). In most cases, parallel corpora contain texts in only two languages but also there are examples of one-language parallel corpora containing a collection of different editions of the same text in one language. In Serbia, JeRTeh, Language Resources and Technologies Society (former Group for Language Technologies) has been developing parallel corpora containing Serbian texts for decades. Until today, JeRTeh has developed: Serbian-French aligned corpus (SrpFranKor) and Serbian-English aligned corpus (SrpEngKor), digital library Biblisha with several parallel collections, and multilingual edition Multilingual Vern. In addition, corpora texts in the Serbian language are part of multilingual parallel corpora Plato’s Republic and Orwell’s 1984 developed during the international projects, as well as part of some corpora developing now in the region and the world. This paper presents corpora developed by Group for Language Technologies, their structure and purpose, as well as possibilities for information retrieval in them. [ABSTRACT FROM AUTHOR]

Currently, the software development industry presents challenges for processing business information, specifically that contained in textual documents. In the process of software requirements elicitation, a potential source of relevant information is business documents, since they can facilitate the knowledge understanding about a domain, as well as know the evolution of a product. Despite its usefulness, requirements engineers do not always use it for their work because of time and costs involved. In this paper this problem is addressed and it is recognized through a systematic literature review, the potentiality of using Natural Language Processing (NLP) techniques to extract relevant textual information from business documents, and the utility of its representation in conceptual models. Starting from this, a semiautomatic method of extracting information from business documents written in natural language in Spanish and its representation in a conceptual model is proposed. The method is supported in a reference methodological framework for Text Analytics projects, is based on NLP techniques, and the output is represented in a class diagram. The method was evaluated through a case study with software analysts in Medellin-Colombia, taking as input telecommunications resolution documents. The evaluation allows us to conclude that the model is a satisfactory approach to solving the problem, and some lines of work are identified to generalize a solution. [ABSTRACT FROM AUTHOR]

Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE. [ABSTRACT FROM AUTHOR]

This study aims to explore research collaborations and authorship patterns in the field of semantic digital libraries(SDL). The data is extracted (N=2075) from the Scopus database using keywords related to semantic digital libraries by considering all types of publications during 1983-2019. The analysis of each document is based on the following scientometrics indicators: author productivity, degree of collaboration, collaboration index, collaboration coefficient and modified collaboration coefficient. Correlation matrices were also calculated and inferences drawn in terms of authors and publications. A network visualisation tool VOSviewer was used to present authorship correlation network strength and keyword mapping for a better insight into the emerging areas in the field of SDL. The resulting average degree of collaboration of 0.898 indicates that a large number of publications are multi-authored and that there is a higher level of collaborative research in the field of semantic digital libraries. Meghini C from the Institute of Information Science and Technologies, Italy has produced the highest number of research paper(n=18) whereas Egenhofer MJ found to be a profoundly impacted author with 851 citations on in the studied domain. Results also reveal that the focus areas of research related to SDL include digital libraries, semantic web, ontology, metadata and information retrieval. However, keywords such as natural language processing system, computational linguistics, linked data are also repeated frequently in the published literature, thus revealing the emerging areas of the future research in the domain of SDL. [ABSTRACT FROM AUTHOR]

Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields, like natural language processing, information retrieval and computational biology. Sampled string matching is an efficient approach recently introduced in order to overcome the prohibitive space requirements of an index construction, on the one hand, and drastically reduce searching time for the online solutions, on the other hand. In this paper we present a new algorithm for the sampled string matching problem, based on a characters distance sampling approach. The main idea is to sample the distances between consecutive occurrences of a given pivot character and then to search online the sampled data for any occurrence of the sampled pattern, before verifying the original text. From a theoretical point of view we prove that, under suitable conditions, our solution can achieve both linear worst-case time complexity and optimal average-time complexity. From a practical point of view it turns out that our solution shows a sub-linear behaviour in practice and speeds up online searching by a factor of up to 9, using limited additional space whose amount goes from 11 to 2.8% of the text size, with a gain up to 50% if compared with previous solutions. [ABSTRACT FROM AUTHOR]

With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities. [ABSTRACT FROM AUTHOR]

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%. [ABSTRACT FROM AUTHOR]

Algeria's socio-linguistic situation is known as a complex phenomenon involving several historical, cultural and technological factors. However, there are three languages that are mainly spoken in Algeria (Arabic, Tamazight and French) and they can be mixed in the same sentence (code-switching). Moreover, there are several varieties of dialects that differ from one region to another and sometimes within the same region. This paper aims to provide a new multi-purpose parallel corpus (i.e., DZDC12 corpus), which will serve as a testbed for various natural language processing and information retrieval applications. In particular, it can be a useful tool to study Arabic–French code-switching phenomenon, Algerian Romanized Arabic (Arabizi), different Algerian sub-dialects, sentiment analysis, gender writing style, machine translation, abuse detection, etc. To the best of our knowledge, the proposed corpus is the first of its kind, where the texts are written in Latin script and crawled from Facebook. More specifically, this corpus is organised by gender, region and city, and is transliterated into Arabic script and translated into Modern Standard Arabic. In addition, it is annotated for emotion detection and abuse detection, and annotated at the word level. This article focuses in particular on Algeria's socio-linguistic situation and the effect of social media networks. Furthermore, the general guidelines for the design of DZDC12 corpus are described as well as the dialects clustering over the map. [ABSTRACT FROM AUTHOR]

Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues. [ABSTRACT FROM AUTHOR]

In natural language processing and information retrieval tasks, the bag-of-words model is widely used to represent the semantics of texts. However, it is difficult for machines to sufficiently understand a bag of words as well as the corresponding text without explicit semantic explanation, thus hindering the power of the bag-of-words model in many scenarios. In this paper, we introduce the task of hierarchical conceptual labeling (HCL), which aims to generate a set of conceptual labels with a hierarchy to explicitly explain the semantics of a bag of words, where the candidate labels are selected from a large-scale knowledge base, i.e., Microsoft Concept Graph. To this end, we first propose a denoising algorithm to filter out the noise in a bag of words in advance. Then the hierarchical conceptual labels are generated for the clean bag of words based on a hierarchical clustering algorithm, i.e., Bayesian rose trees. We conduct extensive experiments and prove that (1) the proposed denoising algorithm can effectively delete the noise words from a bag of words, (2) the Bayesian rose trees based algorithm can generate hierarchical conceptual labels for a bag of words with a high accuracy. [ABSTRACT FROM AUTHOR]