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Journal articles on the topic 'Question Answering, Natural Language Processing, Information Retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Veisi, Hadi, and Hamed Fakour Shandi. "A Persian Medical Question Answering System." International Journal on Artificial Intelligence Tools 29, no. 06 (2020): 2050019. http://dx.doi.org/10.1142/s0218213020500190.

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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

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Chandurkar, Avani, and Ajay Bansal. "A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base." International Journal of Semantic Computing 11, no. 03 (2017): 345–71. http://dx.doi.org/10.1142/s1793351x17400141.

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With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language

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Singh, Vaishali, and Sanjay K. Dwivedi. "Question Answering." International Journal of Information Retrieval Research 4, no. 3 (2014): 14–33. http://dx.doi.org/10.4018/ijirr.2014070102.

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With the huge amount of data available on web, it has turned out to be a fertile area for Question Answering (QA) research. Question answering, an instance of information retrieval research is at the cross road from several research communities such as, machine learning, statistical learning, natural language processing and pattern learning. In this paper, the authors survey the research in area of question answering with respect to different prospects of NLP, machine learning, statistical learning and pattern learning. Then they situate some of the prominent QA systems concerning these prospe

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Alsubhi, Kholoud, Amani Jamal, and Areej Alhothali. "Deep learning-based approach for Arabic open domain question answering." PeerJ Computer Science 8 (May 4, 2022): e952. http://dx.doi.org/10.7717/peerj-cs.952.

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Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation

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MONZ, CHRISTOF. "Machine learning for query formulation in question answering." Natural Language Engineering 17, no. 4 (2011): 425–54. http://dx.doi.org/10.1017/s1351324910000276.

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AbstractResearch on question answering dates back to the 1960s but has more recently been revisited as part of TREC's evaluation campaigns, where question answering is addressed as a subarea of information retrieval that focuses on specific answers to a user's information need. Whereas document retrieval systems aim to return the documents that are most relevant to a user's query, question answering systems aim to return actual answers to a users question. Despite this difference, question answering systems rely on information retrieval components to identify documents that contain an answer t

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Christanno, Ivan, Priscilla Priscilla, Jody Johansyah Maulana, Derwin Suhartono, and Rini Wongso. "Eve: An Automated Question Answering System for Events Information." ComTech: Computer, Mathematics and Engineering Applications 8, no. 1 (2017): 15. http://dx.doi.org/10.21512/comtech.v8i1.3781.

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The objective of this research was to create a closed-domain of automated question answering system specifically for events called Eve. Automated Question Answering System (QAS) is a system that accepts question input in the form of natural language. The question will be processed through modules to finally return the most appropriate answer to the corresponding question instead of returning a full document as an output. Thescope of the events was those which were organized by Students Association of Computer Science (HIMTI) in Bina Nusantara University. It consisted of 3 main modules namely q

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Xiao, Yuliang, Lijuan Zhang, Jie Huang, Lei Zhang, and Jian Wan. "An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering." Electronics 11, no. 19 (2022): 3214. http://dx.doi.org/10.3390/electronics11193214.

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Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieva

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Ali, Irphan, Divakar Yadav, and Ashok Kumar Sharma. "SWFQA Semantic Web Based Framework for Question Answering." International Journal of Information Retrieval Research 9, no. 1 (2019): 88–106. http://dx.doi.org/10.4018/ijirr.2019010106.

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A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Ans

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Arbaaeen, Ammar, and Asadullah Shah. "Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review." Information 12, no. 5 (2021): 200. http://dx.doi.org/10.3390/info12050200.

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For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting

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Yogish, Deepa, T. N. Manjunath, and Ravindra S. Hegadi. "Analysis of Vector Space Method in Information Retrieval for Smart Answering System." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4468–72. http://dx.doi.org/10.1166/jctn.2020.9099.

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In the world of internet, searching play a vital role to retrieve the relevant answers for the user specific queries. The most promising application of natural language processing and information retrieval system is Question answering system which provides directly the accurate answer instead of set of documents. The main objective of information retrieval is to retrieve relevant document from a huge volume of data sets underlying in the internet using appropriatemodel. There are many models proposed for retrieval process such as Boolean, Vector space and Probabilistic method. Vector space mod

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Luo, Man, Arindam Mitra, Tejas Gokhale, and Chitta Baral. "Improving Biomedical Information Retrieval with Neural Retrievers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11038–46. http://dx.doi.org/10.1609/aaai.v36i10.21352.

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Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical doma

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Zhu, Xun, and Hong Tao Deng. "Research of Drug Name Entity Recognition Based on Constructed Dictionary and Conditional Random Field." Applied Mechanics and Materials 665 (October 2014): 739–44. http://dx.doi.org/10.4028/www.scientific.net/amm.665.739.

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Drug name entity recognition (NER) is an important foundation of information extraction, automatic question answering, machine translation and information retrieval and other natural language processing technology based on the medical literature. This paper presents a method combined a constructed dictionary and conditional random field model to identify the drug entity. The proposed method has good performance in DDIExtraction 2013 evaluation corpus. //

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Hkiri, Emna, Souheyl Mallat, and Mounir Zrigui. "Events Automatic Extraction from Arabic Texts." International Journal of Information Retrieval Research 6, no. 1 (2016): 36–51. http://dx.doi.org/10.4018/ijirr.2016010103.

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The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

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Almotairi, Maram, and Fethi Fkih. "Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources." Journal of Education and e-Learning Research 9, no. 4 (2022): 224–32. http://dx.doi.org/10.20448/jeelr.v9i4.4201.

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The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes i

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Guda, Vanitha, and SureshKumar Sanampudi. "Event Time Relationship in Natural Language Text." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 3 (2019): 4. http://dx.doi.org/10.3991/ijes.v7i3.10985.

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<p>Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a

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Faiz, Rim, and Nouha Othman. "Retrieving Relevant Passages Using N-grams for Open-Domain Question Answering." International Journal on Artificial Intelligence Tools 28, no. 07 (2019): 1950021. http://dx.doi.org/10.1142/s0218213019500210.

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Question Answering is most likely one of the toughest tasks in the field of Natural Language Processing. It aims at directly returning accurate and short answers to questions asked by users in human language over a huge collection of documents or database. Recently, the continuously exponential rise of digital information has imposed the need for more direct access to relevant answers. Thus, question answering has been the subject of a widespread attention and has been extensively explored over the last few years. Retrieving passages remains a crucial but also a challenging task in question an

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Chen, Qingyu, Robert Leaman, Alexis Allot, et al. "Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing." Annual Review of Biomedical Data Science 4, no. 1 (2021): 313–39. http://dx.doi.org/10.1146/annurev-biodatasci-021821-061045.

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The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)—the branch of artificial intelligence that interprets human language—can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys

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Bovi, Claudio Delli, and Roberto Navigli. "Multilingual semantic dictionaries for natural language processing: The case of BabelNet." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630015. http://dx.doi.org/10.1142/s2425038416300159.

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Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. A complete semantic model of language, however, needs first of all reliable building blocks. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced inc

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Jayalakshmi, S., and Ananthi Sheshaayee. "Exploring the Web and Semantic Knowledge-Driven Automatic Question Answering System." International Journal of Engineering & Technology 7, no. 3.6 (2018): 379. http://dx.doi.org/10.14419/ijet.v7i3.6.16007.

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The growth of information retrieval from the web sources are increased day by day, proving an effective and efficient way to the user for retrieving relevant documents from the web is an art. Asking the right question and retrieving a right answer to the posted query is a service which provide by the Natural Language Processing. Question Answering System is one of the best ways to identify the candidate answer with high accuracy. The web and Semantic Knowledge Driven Question Answering System (QAS) used to determine the candidate answer for the posted query in the NLP tools. This method includ

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Huang, Jingyi. "Research and Applications Analysis of Knowledge Base Question Answering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 16–22. http://dx.doi.org/10.54097/hset.v16i.2067.

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Knowledge Base Question Answering (KBQA) has become one of recent trends in Natural Language Processing (NLP). It helps solve question answering tasks in many fields, such as commerce, medical treatment, etc. This article represents research of KBQA from theory to practice. The concept of knowledge graph in a new way are defined, the steps for building a basic knowledge graph are listed. The category of knowledge base is generalized. This article analyzes the category of knowledge based on systems and introduces the definition and working principle of KBQA. This article also introduces two mai

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Kato, Makoto P., Yiqun Liu, Noriko Kando, and Charles L. A. Clarke. "Report on the 15th round of NII testbeds and community for information access research (NTCIR-15)." ACM SIGIR Forum 55, no. 2 (2021): 1–6. http://dx.doi.org/10.1145/3527546.3527570.

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This is a report on the NTCIR-15 conference held online in December 2020. NTCIR is a sesquiannual research project designed to evaluate various information access technologies, including information retrieval, information recommendation, question answering, natural language processing, etc. 55 active research groups from 22 countries\regions have participated in one or more of the seven tasks in NTCIR-15. This report introduces the highlights of the conference, describes the scope and task designs of the seven tasks organized at NTCIR-15. Date : 8--11 December, 2020. Website : http://research.

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Park, Dongju, and Chang Wook Ahn. "Self-Supervised Contextual Data Augmentation for Natural Language Processing." Symmetry 11, no. 11 (2019): 1393. http://dx.doi.org/10.3390/sym11111393.

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In this paper, we propose a novel data augmentation method with respect to the target context of the data via self-supervised learning. Instead of looking for the exact synonyms of masked words, the proposed method finds words that can replace the original words considering the context. For self-supervised learning, we can employ the masked language model (MLM), which masks a specific word within a sentence and obtains the original word. The MLM learns the context of a sentence through asymmetrical inputs and outputs. However, without using the existing MLM, we propose a label-masked language

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Vakulenko, Svitlana, Ondřej Dušek, and Leigh Clark. "Report on the 6th workshop on search-oriented conversational AI (SCAI 2021)." ACM SIGIR Forum 55, no. 2 (2021): 1–14. http://dx.doi.org/10.1145/3527546.3527569.

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The 6th edition of the Search-Oriented Conversational AI workshop (SCAI 2021) was organised as a discussion platform on conversational AI for intelligent information access. The workshop was designed to be multidisciplinary, bringing together researchers and practitioners across the fields of natural language processing (NLP), information retrieval (IR), machine learning (ML) and human-computer interaction (HCI). The workshop included four sessions featuring invited talks, a separate poster session, and a session discussing the results of a shared task on conversational question answering (SCA

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Renjit, Sara, and Sumam Idicula. "Natural language inference for Malayalam language using language agnostic sentence representation." PeerJ Computer Science 7 (May 4, 2021): e508. http://dx.doi.org/10.7717/peerj-cs.508.

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Natural language inference (NLI) is an essential subtask in many natural language processing applications. It is a directional relationship from premise to hypothesis. A pair of texts is defined as entailed if a text infers its meaning from the other text. The NLI is also known as textual entailment recognition, and it recognizes entailed and contradictory sentences in various NLP systems like Question Answering, Summarization and Information retrieval systems. This paper describes the NLI problem attempted for a low resource Indian language Malayalam, the regional language of Kerala. More tha

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Biswas, Sitanath, and Sujata Dash. "A Hybrid Bootstrapping Approach for developing Odiya Named Entity Corpora from Wikipedia." International Journal of Engineering & Technology 7, no. 4.38 (2018): 11. http://dx.doi.org/10.14419/ijet.v7i4.38.24311.

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Named Entity Recognition (NER) is considered as very influential undertaking in natural language processing appropriate to Question Answering system, Machine Translation (MT), Information extraction (IE), Information Retrieval (IR) etc. Basically NER is to identify and classify different types of proper nouns present inside given file like location name, person name, number, organization name, time etc. Although huge amount of progress is made for different Indian languages, NER is still a big problem for Odiya Language. Odiya is also a resource constrained language and till today, this is ver

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Moholkar, Kavita, and S. H. Patil. "Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 01 (2022): 93–114. http://dx.doi.org/10.1142/s0218488522500052.

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Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in it

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He, Ming, Zhen Zhen Wang, and Yong Ping Du. "Document Similarity Measure Based on Topic Model." Applied Mechanics and Materials 513-517 (February 2014): 1280–84. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1280.

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Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing (NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic model-based method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study u

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Yan, Yan, Bo-Wen Zhang, Xu-Feng Li, and Zhenhan Liu. "List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders." PLOS ONE 15, no. 11 (2020): e0242061. http://dx.doi.org/10.1371/journal.pone.0242061.

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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 o

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Mars, Mourad. "From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough." Applied Sciences 12, no. 17 (2022): 8805. http://dx.doi.org/10.3390/app12178805.

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With the recent advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. In this paper, we provide a comprehensive review of prior embedding models as well as current breakthroughs in the field of PLMs. Then, we analyse and contrast the various models and provide an

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Surdeanu, Mihai, Massimiliano Ciaramita, and Hugo Zaragoza. "Learning to Rank Answers to Non-Factoid Questions from Web Collections." Computational Linguistics 37, no. 2 (2011): 351–83. http://dx.doi.org/10.1162/coli_a_00051.

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This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question–answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively. We investigate a wide range of feature types, some exploiting natural language processing such as coarse word sense disambiguation, named-entity identification, syntactic parsing, and semantic role labeling. Our experiments demonstrat

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Wang, Jiapeng, and Yihong Dong. "Measurement of Text Similarity: A Survey." Information 11, no. 9 (2020): 421. http://dx.doi.org/10.3390/info11090421.

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Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the

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Deepthi, Godavarthi, and A. Mary Sowjanya. "Query-Based Retrieval Using Universal Sentence Encoder." Revue d'Intelligence Artificielle 35, no. 4 (2021): 301–6. http://dx.doi.org/10.18280/ria.350404.

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the pr

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Lu, Zhao-Hua, Jade Xiaoqing Wang, and Xintong Li. "Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study." Journal of Medical Internet Research 23, no. 3 (2021): e22860. http://dx.doi.org/10.2196/22860.

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Background COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. Objective A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. Me

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Zhang, Peng, Wenjie Hui, Benyou Wang, et al. "Complex-valued Neural Network-based Quantum Language Models." ACM Transactions on Information Systems 40, no. 4 (2022): 1–31. http://dx.doi.org/10.1145/3505138.

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Language modeling is essential in Natural Language Processing and Information Retrieval related tasks. After the statistical language models, Quantum Language Model (QLM) has been proposed to unify both single words and compound terms in the same probability space without extending term space exponentially. Although QLM achieved good performance in ad hoc retrieval, it still has two major limitations: (1) QLM cannot make use of supervised information, mainly due to the iterative and non-differentiable estimation of the density matrix, which represents both queries and documents in QLM. (2) QLM

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Greiner-Petter, André, Abdou Youssef, Terry Ruas, et al. "Math-word embedding in math search and semantic extraction." Scientometrics 125, no. 3 (2020): 3017–46. http://dx.doi.org/10.1007/s11192-020-03502-9.

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AbstractWord embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate des

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Mahmoud, Adnen, and Mounir Zrigui. "Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic." International Arab Journal of Information Technology 18, no. 1 (2020): 1–7. http://dx.doi.org/10.34028/iajit/18/1/1.

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Paraphrase detection allows determining how original and suspect documents convey the same meaning. It has attracted attention from researchers in many Natural Language Processing (NLP) tasks such as plagiarism detection, question answering, information retrieval, etc., Traditional methods (e.g., Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Latent Semantic Analysis (LSA)) cannot capture efficiently hidden semantic relations when sentences may not contain any common words or the co-occurrence of words is rarely present. Therefore, we proposed a deep

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Guan, Xiaohan, Jianhui Han, Zhi Liu, and Mengmeng Zhang. "Sentence Similarity Algorithm Based on Fused Bi-Channel Dependency Matching Feature." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (2019): 2050019. http://dx.doi.org/10.1142/s0218001420500196.

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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 s

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A P, Ajees, Manju K, and Sumam Mary Idicula. "An Improved Word Representation for Deep Learning Based NER in Indian Languages." Information 10, no. 6 (2019): 186. http://dx.doi.org/10.3390/info10060186.

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Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions we

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Zhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.

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Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, know

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Kügler, Patricia, Max Marian, Rene Dorsch, Benjamin Schleich, and Sandro Wartzack. "A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology." Lubricants 10, no. 2 (2022): 18. http://dx.doi.org/10.3390/lubricants10020018.

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Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decre

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Ekbal, Asif, and Sivaji Bandyopadhyay. "Named entity recognition in Bengali and Hindi using support vector machine." Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources 34, no. 1 (2011): 35–67. http://dx.doi.org/10.1075/li.34.1.02ekb.

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Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity (NE) classes and is nowadays considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes, defined as part of

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Wu, Xiaohua, Tengrui Wang, Youping Fan, and Fangjian Yu. "Chinese Event Extraction via Graph Attention Network." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (2022): 1–12. http://dx.doi.org/10.1145/3494533.

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Event extraction plays an important role in natural language processing (NLP) applications, including question answering and information retrieval. Most of the previous state-of-the-art methods were lack of ability in capturing features in long range. Recent methods applied dependency tree via dependency-bridge and attention-based graph. However, most of the automatic processing tools used in those methods show poor performance on Chinese texts due to mismatching between word segmentation and labels, which results in error propagation. In this article, we propose a novel character-level C hine

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Kandasamy, Saravanakumar, and Aswani Kumar Cherukuri. "LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet." Journal of Information & Knowledge Management 20, no. 01 (2021): 2150006. http://dx.doi.org/10.1142/s0219649221500064.

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Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach t

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Ali, Wajid, Wanli Zuo, Ying Wang, and Rahman Ali. "Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining." Applied Sciences 13, no. 5 (2023): 3047. http://dx.doi.org/10.3390/app13053047.

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In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this

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Nguyen, Bao-An, and Don-Lin Yang. "A semi-automatic approach to construct Vietnamese ontology from online text." International Review of Research in Open and Distributed Learning 13, no. 5 (2012): 148. http://dx.doi.org/10.19173/irrodl.v13i5.1250.

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An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology. However, due to the l

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Griffon, N., J. Charlet, and SJ Darmoni. "Knowledge Representation and Management: Towards an Integration of a Semantic Web in Daily Health Practice." Yearbook of Medical Informatics 22, no. 01 (2013): 155–58. http://dx.doi.org/10.1055/s-0038-1638847.

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Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles. Results: Among the four selected articles (see Table 1), one focuses on kno

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Vaghasia, Rishil. "An Improvised Approach of Deep Learning Neural Networks in NLP Applications." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 1599–603. http://dx.doi.org/10.22214/ijraset.2023.48884.

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Abstract: In recent years, natural language processing (NLP) has drawn a lot of interest for its ability to computationally represent and analyze human language. Its uses have expanded to include machine translation, email spam detection, information extraction, summarization, medical diagnosis, and question answering, among other areas. The purpose of this research is to investigate how deep learning and neural networks are used to analyze the syntax of natural language. This research first investigates a feed-forward neural network-based classifier for a transfer-based dependent syntax analy

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Al-Matham, Rawan N., and Hend S. Al-Khalifa. "SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings." Complexity 2021 (February 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6627434.

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Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, wh

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Agrawal, Ankit, Sarsij Tripathi, Manu Vardhan, Vikas Sihag, Gaurav Choudhary, and Nicola Dragoni. "BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling." Applied Sciences 12, no. 3 (2022): 976. http://dx.doi.org/10.3390/app12030976.

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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learni

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Gupta, Dhruv. "Search and analytics using semantic annotations." ACM SIGIR Forum 53, no. 2 (2019): 100–101. http://dx.doi.org/10.1145/3458553.3458567.

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Current information retrieval systems are limited to text in documents for helping users with their information needs. With the progress in the field of natural language processing, there now exists the possibility of enriching large document collections with accurate semantic annotations. Annotations in the form of part-of-speech tags, temporal expressions, numerical values, geographic locations, and other named entities can help us look at terms in text with additional semantics. This doctoral dissertation presents methods for search and analysis of large semantically annotated document coll

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