Relevant bibliographies by topics / Natural Language Processing (NLP)

Contents

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

Academic literature on the topic 'Natural Language Processing (NLP)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Natural Language Processing (NLP)"

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Jayanth, Chikkarangaiah, Uday Adarsh, De Hilster David, Gangadhar Shobha, and Shetty Jyoti. "Enhancing the English natural language processing dictionary using natural language processing++." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3466–77. https://doi.org/10.11591/ijai.v13.i3.pp3466-3477.

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Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enhancement of the English dictionary
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G, Dr Chandrika. "Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1092–95. http://dx.doi.org/10.22214/ijraset.2024.63281.

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Abstract: Machines utilize Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), to comprehend, evaluate, and translate human languages. Due to advancements in information and communication technology over the last ten years, NLP has gained increased awareness and has been the subject of several studies. Understanding the steps required to advance literary understanding is so crucial. The study's objective is to provide an organized review of the literature in NLP research using bibliometric analysis. In the discipline of NLP, the study highlights important research fi
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Chikkarangaiah, Jayanth, Adarsh Uday, David De Hilster, Shobha Gangadhar, and Jyoti Shetty. "Enhancing the English natural language processing dictionary using natural language processing++." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3466. http://dx.doi.org/10.11591/ijai.v13.i3.pp3466-3477.

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<p><span lang="EN-US">Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enha
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Shamsitdinova, M. "Incorporating Natural Language Processing Technologies for Personalized Language Learning Experience." International Journal of Law And Criminology 5, no. 5 (2025): 79–80. https://doi.org/10.37547/ijlc/volume05issue05-11.

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This paper explores the application of Natural Language Processing (NLP) technologies in delivering personalized language learning experiences. As language learning evolves with digital transformation, NLP stands out as a powerful tool for customizing content, automating feedback, and enhancing learner engagement. The study discusses current NLP techniques, their integration into language education platforms, and the pedagogical benefits of adaptive learning systems. It concludes with challenges and future prospects of implementing NLP in educational settings.
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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of th
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Németh, Renáta, and Júlia Koltai. "Natural language processing." Intersections 9, no. 1 (2023): 5–22. http://dx.doi.org/10.17356/ieejsp.v9i1.871.

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Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches. In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some
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Rohit, Kumar Yadav, Madaan Aanchal, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 083–90. https://doi.org/10.5281/zenodo.13690773.

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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, t
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Rohit Kumar Yadav, Aanchal Madaan, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 083–90. http://dx.doi.org/10.30574/gjeta.2024.19.1.0058.

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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, t
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Sadiku, Matthew N. O., Yu Zhou, and Sarhan M. Musa. "NATURAL LANGUAGE PROCESSING IN HEALTHCARE." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (2018): 39. http://dx.doi.org/10.23956/ijarcsse.v8i5.626.

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Natural language processing (NLP) refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication. Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind. This paper provides a brief introduction on the use of NLP in healthcare.
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Alharbi, Mohammad, Matthew Roach, Tom Cheesman, and Robert S. Laramee. "VNLP: Visible natural language processing." Information Visualization 20, no. 4 (2021): 245–62. http://dx.doi.org/10.1177/14738716211038898.

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In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and pa
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Dissertations / Theses on the topic "Natural Language Processing (NLP)"

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Hellmann, Sebastian. "Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-157932.

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This thesis is a compendium of scientific works and engineering specifications that have been contributed to a large community of stakeholders to be copied, adapted, mixed, built upon and exploited in any way possible to achieve a common goal: Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data The explosion of information technology in the last two decades has led to a substantial growth in quantity, diversity and complexity of web-accessible linguistic data. These resources become even more useful when linked with each other and the last few years have see
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NOZZA, DEBORA. "Deep Learning for Feature Representation in Natural Language Processing." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2018. http://hdl.handle.net/10281/241185.

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La mole di dati generata dagli utenti sul Web è esponenzialmente cresciuta negli ultimi dieci anni, creando nuove e rilevanti opportunità per ogni tipo di dominio applicativo. Per risolvere i problemi derivanti dall’eccessiva quantità di dati, la ricerca nell’ambito dell’elaborazione del linguaggio naturale si è mossa verso lo sviluppo di modelli computazionali capaci di capirlo ed interpretarlo senza (o quasi) alcun intervento umano. Recentemente, questo campo di studi è stato testimone di un incremento sia in termini di efficienza computazionale che di risultati, per merito dell’avvento di
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Panesar, Kulvinder. "Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective." Vernon Press, 2020. http://hdl.handle.net/10454/18140.

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Yes<br>This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based me
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Välme, Emma, and Lea Renmarker. "Accelerating Sustainability Report Assessment with Natural Language Processing." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445912.

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Corporations are expected to be transparent on their sustainability impact and keep their stakeholders informed about how large the impact on the environment is, as well as their work on reducing the impact in question. The transparency is accounted for in a, usually voluntary, sustainability report additional to the already required financial report. With new regulations for mandatory sustainability reporting in Sweden, comprehensive and complete guidelines for corporations to follow are insufficient and the reports tend to be extensive. The reports are therefore hard to assess in terms of ho
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Djoweini, Camran, and Henrietta Hellberg. "Approaches to natural language processing in app development." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230167.

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Natural language processing is an on-going field that is not yet fully established. A high demand for natural language processing in applications creates a need for good development-tools and different implementation approaches developed to suit the engineers behind the applications. This project approaches the field from an engineering point of view to research approaches, tools, and techniques that are readily available today for development of natural language processing support. The sub-area of information retrieval of natural language processing was examined through a case study, where pr
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Sætre, Rune. "GeneTUC: Natural Language Understanding in Medical Text." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-545.

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<p>Natural Language Understanding (NLU) is a 50 years old research field, but its application to molecular biology literature (BioNLU) is a less than 10 years old field. After the complete human genome sequence was published by Human Genome Project and Celera in 2001, there has been an explosion of research, shifting the NLU focus from domains like news articles to the domain of molecular biology and medical literature. BioNLU is needed, since there are almost 2000 new articles published and indexed every day, and the biologists need to know about existing knowledge regarding their own researc
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Andrén, Samuel, and William Bolin. "NLIs over APIs : Evaluating Pattern Matching as a way of processing natural language for a simple API." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186429.

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This report explores of the feasibility of using pattern matching for implementing a robust Natural Language Interface (NLI) over a limited Application Programming Interface (API). Because APIs are used to such a great extent today and often in mobile applications, it becomes more important to find simple ways of making them accessible to end users. A very intuitive way to access information via an API is using natural language. Therefore, this study first explores the possibility of building a corpus of the most common phrases used for a particular API. It is then explored how those phrases a
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Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.

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Pharmacovigilance, also referred to as drug safety, is an important science for identifying risks related to medicine intake. Side effects of medicine can be caused by for example interactions, high dosage and misuse. In order to find patterns in what causes the unwanted effects, information needs to be gathered and mapped to predefined terms. This mapping is today done manually by experts which can be a very difficult and time consuming task. In this thesis the aim is to automate the process of mapping side effects by using machine learning techniques. The model was developed using informatio
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Huang, Fei. "Improving NLP Systems Using Unconventional, Freely-Available Data." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/221031.

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Computer and Information Science<br>Ph.D.<br>Sentence labeling is a type of pattern recognition task that involves the assignment of a categorical label to each member of a sentence of observed words. Standard supervised sentence-labeling systems often have poor generalization: it is difficult to estimate parameters for words which appear in the test set, but seldom (or never) appear in the training set, because they only use words as features in their prediction tasks. Representation learning is a promising technique for discovering features that allow a supervised classifier to generalize fr
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Woldemariam, Yonas Demeke. "Natural language processing in cross-media analysis." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-147640.

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A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Wi
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Books on the topic "Natural Language Processing (NLP)"

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Christodoulakis, Dimitris N., ed. Natural Language Processing — NLP 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45154-4.

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International Conference on Natural Language Processing (2nd 2000 Patrai, Greece). Natural language processing - NLP 2000: Second International Conference, Patras, Greece, June 2-4, 2000 : proceedings. Springer, 2000.

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Gurevych, Iryna. The People’s Web Meets NLP: Collaboratively Constructed Language Resources. Springer Berlin Heidelberg, 2013.

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Oppentocht, Anna Linnea. Lexical semantic classification of Dutch verbs: Towards constructing NLP and human-friendly definitions. LEd, 1999.

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Chengqing, Zong, Chinese Association for Artificial Intelligence., IEEE Signal Processing Society, IEEE Systems, Man, and Cybernetics Society, and Institute of Electrical and Electronics Engineers. Beijing Section., eds. 2003 International conference on natural language processing and knowledge engineering: Proceedings : NLP-KE 2003 : Beijing, China. IEEE, 2003.

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International Conference on Natural Language Processing and Knowledge Engineering (2007 Beijing, China). Proceedings of International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'07) : Aug. 30-Sep. 1, Beijing China. IEEE, 2007.

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Loftsson, Hrafn, Eiríkur Rögnvaldsson, and Sigrún Helgadóttir, eds. Advances in natural language processing: 7th International Conference on NLP, IceTAL 2010, Reykjavik, Iceland, August 16-18, 2010 : proceedings. Springer, 2010.

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Kyoko, Kanzaki, and SpringerLink (Online service), eds. Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Boschetti Adamczyk, Willian, Verónica Escudero, Hannah Liepmann, Simon Boehmer, and Isaure Delaporte. Developing a new method to uncover skills trends in emerging economies using online data and NLP techniques. ILO, 2025. https://doi.org/10.54394/hqqx3200.

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This research brief outlines a novel methodology using online big data and natural language processing (NLP) to analyze transferable skills trends across diverse sectors and countries, offering insights into skills demand, supply, mismatches, and their relationship to job quality and transitions.
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Boschetti Adamczyk, Willian, Isaure Delaporte, and Verónica Escudero. Measuring quality of employment in emerging economies. ILO, 2025. https://doi.org/10.54394/ynll0477.

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Book chapters on the topic "Natural Language Processing (NLP)"

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Lee, Raymond S. T. "Major NLP Applications." In Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1999-4_9.

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Lee, Raymond. "Major NLP Applications." In Natural Language Processing. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3208-4_9.

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Steedman, Mark. "Connectionist and symbolist sentence processing." In Natural Language Processing. John Benjamins Publishing Company, 2002. http://dx.doi.org/10.1075/nlp.4.07ste.

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Bunt, Harry, and William Black. "The ABC of Computational Pragmatics." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.01bun.

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Allwood, Jens. "An activity-based approach to pragmatics." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.02all.

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Bunt, Harry. "Dialogue pragmatics and context specification." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.03bun.

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Sabah, Gérard. "Pragmatics in language understanding and cognitively motivated architectures." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.04sab.

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Taylor, Martin M., and David A. Waugh. "Dialogue analysis using layered protocols." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.05tay.

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Redeker, Gisela. "Coherence and structure in text and discourse." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.06red.

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Carter, David. "Discourse focus tracking." In Natural Language Processing. John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.07car.

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Conference papers on the topic "Natural Language Processing (NLP)"

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Pabalkar, Sarika, Prashant Patel, Ramila Choudhary, Vanshika Panoch, Sahil Yadav, and Harsh Ghogale. "Resume Analyzer Using Natural Language Processing (NLP)." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828940.

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Braun, Daniel. "Teaching Natural Language Processing in Law School." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.13.

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McCrae, John. "Co-Creational Teaching of Natural Language Processing." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.5.

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Singh, Dipali. "Legal documents Text Analysis using Natural Language Processing (NLP)." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760929.

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Parde, Natalie. "Example-Driven Course Slides on Natural Language Processing Concepts." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.2.

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Das, Satyesh, and Divyesh Das. "Natural Language Processing (NLP) Techniques: Usability in Human-Computer Interactions." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692776.

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Askarov, Mirzokhid, Alisher Gafforov, Adolat Darmonova, Mokhlaroyim Dadakhonova, Tokhirjon Ismailov, and Ugiljon Qushnazarova. "Preserving Historical Documents Using OCR and Natural Language Processing (NLP)." In 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE, 2025. https://doi.org/10.1109/iccies63851.2025.11032769.

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Malaysha, Sanad, Mo El-Haj, Saad Ezzini, et al. "AraFinNLP 2024: The First Arabic Financial NLP Shared Task." In Proceedings of The Second Arabic Natural Language Processing Conference. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.arabicnlp-1.34.

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Aßenmacher, Matthias, Andreas Stephan, Leonie Weissweiler, et al. "Collaborative Development of Modular Open Source Educational Resources for Natural Language Processing." In Proceedings of the Sixth Workshop on Teaching NLP. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.teachingnlp-1.6.

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Francis, Jibin, and M. Subha. "An Overview of Natural Language Processing (NLP) in Healthcare: Implications for English Language Teaching." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714890.

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Reports on the topic "Natural Language Processing (NLP)"

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Liang, Xiao. Analyzing the Amazon Shopping Experience: A Sentiment Analysis Based on Natural Language Processing (NLP) and Model Comparison. Iowa State University, 2024. http://dx.doi.org/10.31274/cc-20240624-215.

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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Banco de España, 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses f
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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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Avellán, Leopoldo, and Steve Brito. Crossroads in a Fog: Navigating Latin America's Development Challenges with Text Analytics. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005489.

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Latin America and the Caribbean are facing challenging times due to a combination of worsening development gaps and limited fiscal space to address them. Furthermore, the region is contending with an unfavorable external environment. Issues such as rising poverty, climate change, inadequate infrastructure, and low-quality education and health services, among others, require immediate attention. Deciding how to prioritize efforts to address these development gaps is challenging due to their complexity and urgency, and setting priorities becomes even more difficult when resources are limited. Th
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Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
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Steedman, Mark. Natural Language Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290396.

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7

Bergeaud, Antonin, Adam Jaffe, and Dimitris Papanikolaou. Natural Language Processing and Innovation Research. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33821.

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8

Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603814.

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9

Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada245893.

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Firpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/15015192.

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