Готові списки джерел за темами / Natural language processing (Computer science)

Добірка наукової літератури з теми "Natural language processing (Computer science)"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 29 липня 2024

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Статті в журналах з теми "Natural language processing (Computer science)"

Geetha, Dr V., Dr C. K. Gomathy, Mr D. Sri Datta Vallab Yaratha Yagn, and Sai Praneesh. "THE ROLE OF NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27094.

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Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-based systems to contemporary deep learning models. Natural Language Processing is a subfield of computer science and artificial intelligence that deals with the interactions between computers and humans using natural language. It focuses on the ability of computers to understand, interpret, and generate human language. Keywords Natural Language Processing, Text Analysis , Text Mining , Speech Recognition.

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Wilks, Yorick. "Natural language processing." Communications of the ACM 39, no. 1 (January 1996): 60–62. http://dx.doi.org/10.1145/234173.234180.

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Selfridge, Mallory. "Natural language processing." Artificial Intelligence in Engineering 2, no. 1 (January 1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90076-8.

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Prema, M., Dr V. Raju, and M. Ramya. "Natural Language Processing for Data Science Workforce Analysis." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 13, no. 4 (December 30, 2022): 225–32. http://dx.doi.org/10.58346/jowua.2022.i4.015.

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As the demand for people with Data Science and Data Analysis skills are rising at a very high rate, periodic exploration of the skill sets for jobs in these fields have become essential. This research presents the use of Natural Language Processing for Human Resource Management. It presents the application of such techniques and tools as Python Libraries with Beautiful Soup and Selenium, Web Scrapping, Topic Analysis, Sentiment Analysis, and Natural Language Processing in the identification of skill sets related to Data Scientist, Data Analyst and Data Engineer.

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Cunliffe, Daniel, Andreas Vlachidis, Daniel Williams, and Douglas Tudhope. "Natural language processing for under-resourced languages: Developing a Welsh natural language toolkit." Computer Speech & Language 72 (March 2022): 101311. http://dx.doi.org/10.1016/j.csl.2021.101311.

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Zheng, Haotian, Kangming Xu, Huiming Zhou, Yufu Wang, and Guangze Su. "Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis." Academic Journal of Science and Technology 10, no. 1 (March 26, 2024): 62–68. http://dx.doi.org/10.54097/v160aa61.

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Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In the process of concrete implementation, it is necessary to process natural language at multiple levels. Based on this, this paper combined deep learning and natural language processing technology to conduct sentiment analysis on patients' comments, so as to recommend drugs that are more suitable for patients, thus achieving accurate drug prescribing and personalized recommendation.

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Heidorn, P. Bryan. "Natural language processing." Information Processing & Management 32, no. 1 (January 1996): 122–23. http://dx.doi.org/10.1016/s0306-4573(96)90089-8.

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Søgaard, Anders. "Explainable Natural Language Processing." Synthesis Lectures on Human Language Technologies 14, no. 3 (September 21, 2021): 1–123. http://dx.doi.org/10.2200/s01118ed1v01y202107hlt051.

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Thessen, Anne E., Hong Cui, and Dmitry Mozzherin. "Applications of Natural Language Processing in Biodiversity Science." Advances in Bioinformatics 2012 (May 22, 2012): 1–17. http://dx.doi.org/10.1155/2012/391574.

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Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.

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King, Margaret. "Evaluating natural language processing systems." Communications of the ACM 39, no. 1 (January 1996): 73–79. http://dx.doi.org/10.1145/234173.234208.

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Дисертації з теми "Natural language processing (Computer science)"

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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
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.
by Rachael Naphtal.
M. Eng.

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Cosh, Kenneth John. "Supporting organisational semiotics with natural language processing techniques." Thesis, Lancaster University, 2003. http://eprints.lancs.ac.uk/12351/.

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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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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.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 109-119).
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.
by Tao Lei.
Ph. D.

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Grinman, Alex J. "Natural language processing on encrypted patient data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113438.

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Анотація:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
While many industries can benefit from machine learning techniques for data analysis, they often do not have the technical expertise nor computational power to do so. Therefore, many organizations would benefit from outsourcing their data analysis. Yet, stringent data privacy policies prevent outsourcing sensitive data and may stop the delegation of data analysis in its tracks. In this thesis, we put forth a two-party system where one party capable of powerful computation can run certain machine learning algorithms from the natural language processing domain on the second party's data, where the first party is limited to learning only specific functions of the second party's data and nothing else. Our system provides simple cryptographic schemes for locating keywords, matching approximate regular expressions, and computing frequency analysis on encrypted data. We present a full implementation of this system in the form of a extendible software library and a command line interface. Finally, we discuss a medical case study where we used our system to run a suite of unmodified machine learning algorithms on encrypted free text patient notes.
by Alex J. Grinman.
M. Eng.

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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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Shepherd, David. "Natural language program analysis combining natural language processing with program analysis to improve software maintenance tools /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 176 p, 2007. http://proquest.umi.com/pqdweb?did=1397920371&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Bajwa, Imran Sarwar. "A natural language processing approach to generate SBVR and OCL." Thesis, University of Birmingham, 2014. http://etheses.bham.ac.uk//id/eprint/4890/.

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The Object Constraint Language (OCL) is a declarative language and is used to make the Unified Modeling Language (UML) models well-defined through defining a set of constraints. However, the syntactic complexity of OCL makes the writing of OCL code difficult. A natural language based interface can be useful in making the process of writing OCL expressions easy and simple. However, the translation of natural language (NL) text to object constraint language (OCL) code is a challenging task on account of the informal nature of natural languages as various syntactic and semantic ambiguities make the process of NL translation to formal languages more complex. However, in our approach the usage of SBVR not only provides natural languages a formal abstract syntax representation but it is also close to OCL syntax. In this thesis, a framework is presented to facilitate the users of the UML tools so that they can write invariants and pre/post conditions in English. The results of the case studies manifest that a natural language based approach to generate OCL constraints can not only help in significantly improving usability of OCL but also outperforms the most closely related techniques in terms of effectiveness and effort required in generating OCL.

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Strandberg, Aron, and Patrik Karlström. "Processing Natural Language for the Spotify API : Are sophisticated natural language processing algorithms necessary when processing language in a limited scope?" Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186867.

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Knowing whether you can implement something complex in a simple way in your application is always of interest. A natural language interface is some- thing that could theoretically be implemented in a lot of applications but the complexity of most natural language processing algorithms is a limiting factor. The problem explored in this paper is whether a simpler algorithm that doesn’t make use of convoluted statistical models and machine learning can be good enough. We implemented two algorithms, one utilizing Spotify’s own search and one with a more accurate, o✏ine search. With the best precision we could muster being 81% at an average of 2,28 seconds per query this is not a viable solution for a complete and satisfactory user experience. Further work could push the performance into an acceptable range.

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Bigert, Johnny. "Automatic and unsupervised methods in natural language processing." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156.

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Книги з теми "Natural language processing (Computer science)"

Cohen, Kevin Bretonnel, and Dina Demner-Fushman. Biomedical natural language processing. Amsterdam: John Benjamins Publishing Company, 2014.

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Shwartz, Steven P. Applied natural language processing. Princeton, N.J: Petrocelli Books, 1987.

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Annie, Gal, ed. Prolog for natural language processing. Chichester [England]: Wiley, 1991.

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Tomek, Strzalkowski, ed. Reversible grammar in natural language processing. Boston: Kluwer Acadmic Publishers, 1994.

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Indurkhya, Nitin. Handbook of natural language processing. Boca Raton, FL: Chapman & Hall/CRC, 2010.

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1959-, Dale Robert, Moisl Hermann 1949-, and Somers H. L, eds. Handbook of natural language processing. New York: Marcel Dekker, 2000.

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1939-, Wilks Yorick, ed. Theoretical issues in natural language processing. Hillsdale, N.J: L. Erlbaum, 1989.

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Siddiqui, Tanveer. Natural language processing and information retrieval. New Delhi: Oxford Univ Press, 2008.

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Siddiqui, Tanveer. Natural language processing and information retrieval. New Delhi: Oxford Univ Press, 2008.

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Siddiqui, Tanveer. Natural language processing and information retrieval. New Delhi: Oxford Univ Press, 2008.

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Частини книг з теми "Natural language processing (Computer science)"

Kacalak, Wojciech, Keith Douglas Stuart, and Maciej Majewski. "Intelligent Natural Language Processing." In Lecture Notes in Computer Science, 584–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_79.

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Igual, Laura, and Santi Seguí. "Basics of Natural Language Processing." In Undergraduate Topics in Computer Science, 195–210. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-48956-3_10.

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Hrycej, Tomas, Bernhard Bermeitinger, Matthias Cetto, and Siegfried Handschuh. "Specific Problems of Natural Language Processing." In Texts in Computer Science, 167–94. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19074-2_6.

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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, 256–69. Berlin, Heidelberg: 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, 742–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_93.

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Sbattella, Licia, and Roberto Tedesco. "Knowledge Extraction from Natural Language Processing." In Lecture Notes in Computer Science, 193–219. Berlin, Heidelberg: 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, 508–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31753-8_57.

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Barbero, Cristina, and Vincenzo Lombardo. "Dependency graphs in natural language processing." In Lecture Notes in Computer Science, 115–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60437-5_11.

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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, 3–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34989-9_1.

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Blache, Philippe. "Constraints, Linguistic Theories, and Natural Language Processing." In Lecture Notes in Computer Science, 221–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45154-4_21.

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Тези доповідей конференцій з теми "Natural language processing (Computer science)"

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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López-Ostenero, Fernando, Laura Plaza, Juan Martinez-Romo, and Lourdes Araujo. "NATURAL LANGUAGE PROCESSING FOR DATA MINING IN COMPUTER SCIENCE EDUCATION." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.0731.

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Percovich, Analia, Alejandro Tosi, Luis Chiruzzo, and Aiala Rosa. "Ludic Applications for Language Teaching Support using Natural Language Processing." In 2019 38th International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2019. http://dx.doi.org/10.1109/sccc49216.2019.8966429.

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Rager, John E. "Two-level grammars and robustness in natural language processing." In the 22nd annual ACM computer science conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/197530.197656.

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Ayanzadeh, Ramin. "Quantum Artificial Intelligence for Natural Language Processing Applications." In SIGCSE '18: The 49th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159450.3162338.

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Jain, Harsh, and Keshav Mathur. "Natural Language Processing Through Different Classes of Machine Learning." In Fourth International conference on Computer Science & Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4226.

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Zlatareva, Neli, and Devansh Amin. "Processing Natural Language Queries in Semantic Web Applications." In The 7th World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2021. http://dx.doi.org/10.11159/cist21.108.

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Gohad, Ameya, Anay Vyawahare, Anmol Gupta, Kashish Sharma, Keyur Dhage, Nilesh Shelke, and Jagdish Patani. "ATS: Auto Text Summarization using Natural Language Processing." In 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2024. http://dx.doi.org/10.1109/sceecs61402.2024.10482227.

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Yin, Qi-Jin, Shao-Ping Wang, Yi-Nan Miao, and Dou Xin. "Chinese Natural Language Processing Based on Semantic Structure Tree." In 2015 International Conference on Computer Science and Applications (CSA). IEEE, 2015. http://dx.doi.org/10.1109/csa.2015.65.

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Varga, Bernadette, Alina Dia Trambitas-Miron, Andrei Roth, Anca Marginean, Radu Razvan Slavescu, and Adrian Groza. "LELA - A natural language processing system for Romanian tourism." In 2014 Federated Conference on Computer Science and Information Systems. IEEE, 2014. http://dx.doi.org/10.15439/2014f323.

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Звіти організацій з теми "Natural language processing (Computer science)"

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

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Анотація:

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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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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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, March 2021. http://dx.doi.org/10.51593/20200057.

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Анотація:

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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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, June 2024. http://dx.doi.org/10.18235/0013015.

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Анотація:

This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.

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