Relevant bibliographies by topics / Natural language processing (Computer science) – Research

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Natural language processing (Computer science) – Research'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Natural language processing (Computer science) – Research"

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Zhao, Liping, Waad Alhoshan, Alessio Ferrari, et al. "Natural Language Processing for Requirements Engineering." ACM Computing Surveys 54, no. 3 (2021): 1–41. http://dx.doi.org/10.1145/3444689.

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Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.
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Heidorn, P. Bryan. "Natural language processing." Information Processing & Management 32, no. 1 (1996): 122–23. http://dx.doi.org/10.1016/s0306-4573(96)90089-8.

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Li, Yong, Xiaojun Yang, Min Zuo, Qingyu Jin, Haisheng Li, and Qian Cao. "Deep Structured Learning for Natural Language Processing." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (2021): 1–14. http://dx.doi.org/10.1145/3433538.

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The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.
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Mazzei, Daniele, Filippo Chiarello, and Gualtiero Fantoni. "Analyzing Social Robotics Research with Natural Language Processing Techniques." Cognitive Computation 13, no. 2 (2021): 308–21. http://dx.doi.org/10.1007/s12559-020-09799-1.

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Taskin, Zehra, and Umut Al. "Natural language processing applications in library and information science." Online Information Review 43, no. 4 (2019): 676–90. http://dx.doi.org/10.1108/oir-07-2018-0217.

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Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.
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Chen, Xieling, Ruoyao Ding, Kai Xu, Shan Wang, Tianyong Hao, and Yi Zhou. "A Bibliometric Review of Natural Language Processing Empowered Mobile Computing." Wireless Communications and Mobile Computing 2018 (June 28, 2018): 1–21. http://dx.doi.org/10.1155/2018/1827074.

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Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.
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Cambria, Erik, and Bebo White. "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]." IEEE Computational Intelligence Magazine 9, no. 2 (2014): 48–57. http://dx.doi.org/10.1109/mci.2014.2307227.

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Leeson, William, Adam Resnick, Daniel Alexander, and John Rovers. "Natural Language Processing (NLP) in Qualitative Public Health Research: A Proof of Concept Study." International Journal of Qualitative Methods 18 (January 1, 2019): 160940691988702. http://dx.doi.org/10.1177/1609406919887021.

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Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but may be time-consuming, labor-intensive, or prone to bias. Natural language processing (NLP) is a machine learning technique from computer science that uses algorithms to analyze textual data. NLP allows processing of large amounts of data almost instantaneously. As researchers become conversant with NLP, it is becoming more frequently employed outside of computer science and shows promise as a tool to analyze qualitative data in public health. This is a proof of concept paper to evaluate the potential of NLP to analyze qualitative data. Specifically, we ask if NLP can support conventional qualitative analysis, and if so, what its role is. We compared a qualitative method of open coding with two forms of NLP, Topic Modeling, and Word2Vec to analyze transcripts from interviews conducted in rural Belize querying men about their health needs. All three methods returned a series of terms that captured ideas and concepts in subjects’ responses to interview questions. Open coding returned 5–10 words or short phrases for each question. Topic Modeling returned a series of word-probability pairs that quantified how well a word captured the topic of a response. Word2Vec returned a list of words for each interview question ordered by which words were predicted to best capture the meaning of the passage. For most interview questions, all three methods returned conceptually similar results. NLP may be a useful adjunct to qualitative analysis. NLP may be performed after data have undergone open coding as a check on the accuracy of the codes. Alternatively, researchers can perform NLP prior to open coding and use the results to guide their creation of their codebook.
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Smith, Glenn Gordon, Robert Haworth, and Slavko Žitnik. "Computer Science Meets Education: Natural Language Processing for Automatic Grading of Open-Ended Questions in eBooks." Journal of Educational Computing Research 58, no. 7 (2020): 1227–55. http://dx.doi.org/10.1177/0735633120927486.

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We investigated how Natural Language Processing (NLP) algorithms could automatically grade answers to open-ended inference questions in web-based eBooks. This is a component of research on making reading more motivating to children and to increasing their comprehension. We obtained and graded a set of answers to open-ended questions embedded in a fiction novel written in English. Computer science students used a subset of the graded answers to develop algorithms designed to grade new answers to the questions. The algorithms utilized the story text, existing graded answers for a given question and publicly accessible databases in grading new responses. A computer science professor used another subset of the graded answers to evaluate the students’ NLP algorithms and to select the best algorithm. The results showed that the best algorithm correctly graded approximately 85% of the real-world answers as correct, partly correct, or wrong. The best NLP algorithm was trained with questions and graded answers from a series of new text narratives in another language, Slovenian. The resulting NLP algorithm model was successfully used in fourth-grade language arts classes for providing feedback to student answers on open-ended questions in eBooks.
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Suzuki, Kenji. "AI: A New Open Access Journal for Artificial Intelligence." AI 1, no. 2 (2020): 141–42. http://dx.doi.org/10.3390/ai1020007.

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As a branch of computer science, artificial intelligence (AI) attempts to understand the essence of intelligence, and produce new kinds of intelligent machines that can respond in a similar way to human intelligence, with broad research areas of machine and deep learning, data science, reinforcement learning, data mining, knowledge discovery, knowledge reasoning, speech recognition, natural language processing, language recognition, image recognition, computer vision, planning, robotics, gaming, and so on [...]
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Dissertations / Theses on the topic "Natural language processing (Computer science) – Research"

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Ramachandran, Venkateshwaran. "A temporal analysis of natural language narrative text." Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03122009-040648/.

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Imam, Md Kaisar. "Improvements to the complex question answering models." Thesis, Lethbridge, Alta. : University of Lethbridge, c2011, 2011. http://hdl.handle.net/10133/3214.

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In recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result.<br>x, 128 leaves : ill. ; 29 cm
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Shivade, Chaitanya P. "How sick are you?Methods for extracting textual evidence to expedite clinical trial screening." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462810822.

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Hale, Scott A. "Global connectivity, information diffusion, and the role of multilingual users in user-generated content platforms." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:3040a250-c526-4f10-aa9b-25117fd4dea2.

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Internet content and Internet users are becoming more linguistically diverse as more people speaking different languages come online and produce content on user-generated content platforms. Several platforms have emerged as truly global platforms with users speaking many different languages and coming from around the world. It is now possible to study human behavior on these platforms using the digital trace data the platforms make available about the content people are authoring. Network literature suggests that people cluster together by language, but also that there is a small average path length between any two people on most Internet platforms (including two speakers of different languages). If so, multilingual users may play critical roles as bridges or brokers on these platforms by connecting clusters of monolingual users together across languages. The large differences in the content available in different languages online underscores the importance of such roles. This thesis studies the roles of multilingual users and platform design on two large, user-generated content platforms: Wikipedia and Twitter. It finds that language has a strong role structuring each platform, that multilingual users do act as linguistic bridges subject to certain limitations, that the size of a language correlates with the roles its speakers play in cross-language connections, and that there is a correlation between activity and multilingualism. In contrast to the general understanding in linguistics of high levels of multilingualism offline, this thesis finds relatively low levels of multilingualism on Twitter (11%) and Wikipedia (15%). The findings have implications for both platform design and social network theory. The findings suggest design strategies to increase multilingualism online through the identification and promotion of multilingual starter tasks, the discovery of related other-language information, and the promotion of user choice in linguistic filtering. While weak-ties have received much attention in the social networks literature, cross-language ties are often not distinguished from same-language weak ties. This thesis finds that cross-language ties are similar to same-language weak ties in that both connect distant parts of the network, have limited bandwidth, and yet transfer a non-trivial amount of information when considered in aggregate. At the same time, cross-language ties are distinct from same-language weak ties for the purposes of information diffusion. In general cross-language ties are smaller in number than same-language ties, but each cross-language tie may convey more diverse information given the large differences in the content available in different languages and the relative ease with which a multilingual speaker may access content in multiple languages compared to a monolingual speaker.
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Zhu, Haoyu. "The state of network research." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289170.

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In the past decades, networking researchers experienced great changes. Being familiar with the development of networking researches is the first step for most scholars to start their work. The targeted areas, useful documents, and active institutions are helpful to set up the new research. This project is focused on developing an assistant tool based on public accessed papers and information on the Internet that allows researchers to view most cited papers in networking conferences and journals. NLP tools are implemented over crawled full-text in order to classify the papers and extract the keywords. Papers are located based on authors to show the most active countries around the world that are working in this area. References are analyzed to view the most cited topics and detailed paper information. We draw some interesting conclusions from our system, showing that some topics attract more attention in the past decades.<br>Under de senaste decennierna upplevde nätverksundersökningar stora förändringar. Att känna till utvecklingen av nätverksundersökningar är det första steget för de flesta forskare att starta sitt arbete. De riktade områdena, användbara dokument och aktiva institutioner är användbara för att skapa den nya forskningen. Projektet fokuserade på att utveckla ett assistentverktyg baserat på offentliga åtkomstpapper och information via internet. Som gör det möjligt för forskare att se de mest citerade artiklarna i nätverkskonferenser och tidskrifter. NLP- verktyg implementeras över genomsökt fulltext för att klassificera papperet och extrahera nyckelorden. Artiklar är baserade på författare för att visa de mest aktiva länderna runt om i världen som arbetar inom detta område. Hänvisningar analyseras för att se det mest citerade ämnet och detaljerad pappersinformation. Vi drar några intressanta slutsatser från vårt system och visar att något ämne inte lockar till sig mer under de senaste decennierna.
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Wijeratne, Sanjaya. "A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547506375922938.

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Naphtal, Rachael (Rachael M. ). "Natural language processing based nutritional application." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100640.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 67-68).<br>The ability to accurately and eciently track nutritional intake is a powerful tool in combating obesity and other food related diseases. Currently, many methods used for this task are time consuming or easily abandoned; however, a natural language based application that converts spoken text to nutritional information could be a convenient and eective solution. This thesis describes the creation of an application that translates spoken food diaries into nutritional database entries. It explores dierent methods for solving the problem of converting brands, descriptions and food item names into entries in nutritional databases. Specifically, we constructed a cache of over 4,000 food items, and also created a variety of methods to allow refinement of database mappings. We also explored methods of dealing with ambiguous quantity descriptions and the mapping of spoken quantity values to numerical units. When assessed by 500 users entering their daily meals on Amazon Mechanical Turk, the system was able to map 83.8% of the correctly interpreted spoken food items to relevant nutritional database entries. It was also able to nd a logical quantity for 92.2% of the correct food entries. Overall, this system shows a signicant step towards the intelligent conversion of spoken food diaries to actual nutritional feedback.<br>by Rachael Naphtal.<br>M. Eng.
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張少能 and Siu-nang Bruce Cheung. "A concise framework of natural language processing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31208563.

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Cheung, Siu-nang Bruce. "A concise framework of natural language processing /." [Hong Kong : University of Hong Kong], 1989. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12432544.

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Lei, Tao Ph D. Massachusetts Institute of Technology. "Interpretable neural models for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108990.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 109-119).<br>The success of neural network models often comes at a cost of interpretability. This thesis addresses the problem by providing justifications behind the model's structure and predictions. In the first part of this thesis, we present a class of sequence operations for text processing. The proposed component generalizes from convolution operations and gated aggregations. As justifications, we relate this component to string kernels, i.e. functions measuring the similarity between sequences, and demonstrate how it encodes the efficient kernel computing algorithm into its structure. The proposed model achieves state-of-the-art or competitive results compared to alternative architectures (such as LSTMs and CNNs) across several NLP applications. In the second part, we learn rationales behind the model's prediction by extracting input pieces as supporting evidence. Rationales are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by the desiderata for rationales. We demonstrate the effectiveness of this learning framework in applications such multi-aspect sentiment analysis. Our method achieves a performance over 90% evaluated against manual annotated rationales.<br>by Tao Lei.<br>Ph. D.
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Books on the topic "Natural language processing (Computer science) – Research"

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Bandyopadhyay, Sivaji, Sudip Kumar Naskar, and Asif Ekbal. Emerging applications of natural language processing: Concepts and new research. Information Science Reference, 2012.

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1959-, Martin James H., ed. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall, 2000.

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1959-, Martin James H., ed. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. 2nd ed. Pearson Prentice Hall, 2008.

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International Conference on Theoretical and Methodological Issues in Machine Translation of Natural Language (3rd 1990 University of Texas at Austin). The Third International Conference on Theoretical and Methodological Issues in Machine Translation of Natural Language: 11-13 June 1990, Linguistics Research Center, University of Texas at Austin. University of Texas at Austin, 1990.

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Krithivasan, Kamala, and R. Rama. Formal language aspects of natural computing: Proceedings of Research Level group discussion meeting on Natural Computing held at Indian Institute of Technology Madras, Chennai, in November 2005. Edited by Ramanujan Mathematical Society. Ramanujan Mathematical Society, India, 2007.

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Applied Natural Language Processing Conference (6th 2000 Seattle, Wash.). Proceedings of the conferences and proceedings of the ANLP-NAACL 2000 student research workshop: 6th Applied Natural Language Processing Conference, 1st Meeting of the North American Chapter of the Association for Computational linguistics. Association for Computational Linguistics, 2000.

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ADS 2004 (2004 Benediktinerabtei Irsee, Germany). Affective dialogue systems: Tutorial and research workshop, ADS 2004, Kloster Irsee, Germany, June 14-16, 2004 ; proceedings. Springer, 2004.

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Hans, Uszkoreit, and SpringerLink (Online service), eds. META-NET Strategic Research Agenda for Multilingual Europe 2020. Springer Berlin Heidelberg, 2013.

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International Symposium on the Standardization of Multilingual Information Technology (4th 1999 Yangon, Burma). Proceedings of MLIT-4: Achievements of the joint development research on international standardization, multilingual information technology. Center of the International Cooperation for Computerization, 2000.

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Applied Natural Language Processing Conference (6th 2000 Seattle, Wash.). 6th Applied Natural Language Processing Conference: 1st Meeting of the North American Chapter of the Association for Computational Linguistics : proceedings of the conferences and Proceedings of the ANLP-NAACL 2000 Student Research Workshop, April 29-May 4, Seattle, Washington, USA. Association for Computational Linguistics, 2000.

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Book chapters on the topic "Natural language processing (Computer science) – Research"

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Chen, Wei-Yu, Shing-Han Li, and Yung-Hsin Wang. "Research on Natural Language Processing in Financial Risk Detection." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_50.

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Kacalak, Wojciech, Keith Douglas Stuart, and Maciej Majewski. "Intelligent Natural Language Processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_79.

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Teufl, Peter, Udo Payer, and Guenter Lackner. "From NLP (Natural Language Processing) to MLP (Machine Language Processing)." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14706-7_20.

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Majewski, Maciej, and Wojciech Kacalak. "Intelligent System for Natural Language Processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_93.

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Barbero, Cristina, and Vincenzo Lombardo. "Dependency graphs in natural language processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60437-5_11.

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Sbattella, Licia, and Roberto Tedesco. "Knowledge Extraction from Natural Language Processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31739-2_10.

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Quarteroni, Silvia. "Natural Language Processing for the Web." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31753-8_57.

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Hernández-Castillo, Carlos, Héctor Hiram Guedea-Noriega, Miguel Ángel Rodríguez-García, and Francisco García-Sánchez. "Pest Recognition Using Natural Language Processing." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34989-9_1.

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Bharati, Akshar, Rajeev Sangal, and Vineet Chaitanya. "Natural language processing, complexity theory and logic." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-53487-3_60.

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Setchi, Rossitza, Qiao Tang, and Lixin Cheng. "Information Retrieval Using Deep Natural Language Processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_117.

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Conference papers on the topic "Natural language processing (Computer science) – Research"

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Wang, Yantao, Yu Sun, and Yuanfei Chen. "Design and Research of Intelligent Tutor System Based on Natural Language Processing." In 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI). IEEE, 2019. http://dx.doi.org/10.1109/csei47661.2019.8939031.

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Yu, Hong Qing. "Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning." In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962507.

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Rašl, Matic, Mitja Žalik, and Vid Keršič. "Transformer-based Sarcasm Detection in English and Slovene Language." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.10.

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Sarcasm detection is an important problem in the field of natural language processing. In this pa-per, we compare performances of the three neural networks for sarcasm detection on English and Slovene datasets. Each network is based on a di˙erent transformer model: RoBERTa, Distil-Bert, and DistilBert – multilingual. In addition to the existing Twitter-based English dataset, we also created the Slovene dataset using the same approach. An F1 score of 0.72 and 0.88 was achieved in the English and Slovene dataset, re-spectively.
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Sundararajan, V. "Constructing a Design Knowledge Base Using Natural Language Processing." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15276.

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Mechanical engineering, like other engineering disciplines, has witnessed maturation of various aspects of its domain, obsolescence of some areas and a resurgence of others. With a history of over 200 years of continuous research and development, both in academia and industry, the community has generated enormous amounts of design knowledge in the form of texts, articles and design drawings. With the advent of electronics and computer science, several of the classical mechanisms faced obsolescence, but with the emergence of MEMS and nanotechnology, the same designs are facing a resurrection. Research and development in mechanical engineering would derive enormous benefit from a structured knowledge-base of designs and mechanisms. This paper describes a prototype system that synthesizes a knowledge-base of mechanical designs by the processing of the text in engineering descriptions. The goal is to construct a system that stores and catalogs engineering designs, their sub-assemblies and their super-assemblies for the purposes of archiving, retrieval for launching new designs and for education of engineering design. Engineering texts have a relatively clear discourse structure with fewer ambiguities, less stylistic variations and less use of complex figures of speech. The text is first passed through a part-of-speech tagger. The concept of thematic roles is used to link different parts of the sentence. The discourse structure is then taken into account by anaphora resolution. The knowledge is gradually built up through progressive scanning and analysis of text. References, interconnections and attributes are added or deleted based upon the nature, reliability and strength of the new information. Examples of analysis and resulting knowledge structures are presented.
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Klima, Gyula. "A Natural Logic for Artificial Intelligence, and its Risks and Benefits." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110710.

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This paper is a multidisciplinary project proposal, submitted in the hopes that it may garner enough interest to launch it with members of the AI research community along with linguists and philosophers of mind and language interested in constructing a semantics for a natural logic for AI. The paper outlines some of the major hurdles in the way of “semantics-driven” natural language processing based on standard predicate logic and sketches out the steps to be taken toward a “natural logic”, a semantic system explicitly defined on a well-regimented (but indefinitely expandable) fragment of a natural language that can, therefore, be “intelligently” processed by computers, using the semantic representations of the phrases of the fragment.
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Chang Wu and Yidong Chen. "A survey of researches on the application of natural language processing in internet public opinion monitor." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5972059.

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Kavi, Deniz. "Towards Adversarial Genetic Text Generation." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110407.

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Text generation is the task of generating natural language, and producing outputs similar to or better than human texts. Due to deep learning’s recent success in the field of natural language processing, computer generated text has come closer to becoming indistinguishable to human writing. Genetic Algorithms have not been as popular in the field of text generation. We propose a genetic algorithm combined with text classification and clustering models which automatically grade the texts generated by the genetic algorithm. The genetic algorithm is given poorly generated texts from a Markov chain, these texts are then graded by a text classifier and a text clustering model. We then apply crossover to pairs of texts, with emphasis on those that received higher grades. Changes to the grading system and further improvements to the genetic algorithm are to be the focus of future research.
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Cujar-Rosero, Felipe, David Santiago Pinchao Ortiz, Silvio Ricardo Timaran Pereira, and Jimmy Mateo Guerrero Restrepo. "Fenix: A Semantic Search Engine Based on an Ontology and a Model Trained with Machine Learning to Support Research." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110709.

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This paper presents the final results of the research project that aimed to build a Semantic Search Engine that uses an Ontology and a model trained with Machine Learning to support the semantic search of research projects of the System of Research from the University of Nariño. For the construction of FENIX, as this Engine is called, it was used a methodology that includes the stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the Semantic Search Engine. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms and specifically Natural Language Processing algorithms were used such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also done in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of FENIX managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that FENIX was successful in all the searches that were carried out because its results were satisfactory.
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Klokov, Aleksey, Evgenii Slobodyuk, and Michael Charnine. "Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms." In International Conference "Computing for Physics and Technology - CPT2020". ANO «Scientific and Research Center for Information in Physics and Technique», 2020. http://dx.doi.org/10.30987/conferencearticle_5fd755c0ea6458.82600196.

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The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment.&#x0D; The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus.&#x0D; The resulting models can be used for semantic processing and analysis of other subject areas.
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Ananiadou, Sophia. "Natural Language Processing for Biomedicine." In The 7th World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2021. http://dx.doi.org/10.11159/cist21.002.

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Reports on the topic "Natural language processing (Computer science) – Research"

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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Murdick, Dewey, Daniel Chou, Ryan Fedasiuk, and Emily Weinstein. The Public AI Research Portfolio of China’s Security Forces. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200057.

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New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.
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