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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "080107 Natural Language Processing"

1

Niimi, Akio. "Natural Language Processing." Chest 159, no. 6 (June 2021): 2149–50. http://dx.doi.org/10.1016/j.chest.2021.01.045.

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2

HASHIDA, KOICHI. "Natural Language Processing." Journal of the Institute of Electrical Engineers of Japan 121, no. 3 (2001): 195–98. http://dx.doi.org/10.1541/ieejjournal.121.195.

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3

N. O. Sadiku, Matthew, Yu Zhou, and Sarhan M. Musa. "Natural Language Processing." International Journal of Advances in Scientific Research and Engineering 4, no. 5 (2018): 68–70. http://dx.doi.org/10.31695/ijasre.2018.32708.

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4

Covington, Michael A., Fernando C. N. Pereira, and Barbara J. Grosz. "Natural Language Processing." Language 71, no. 3 (September 1995): 652. http://dx.doi.org/10.2307/416262.

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5

Jain, Aditya, Gandhar Kulkarni, and Vraj Shah. "Natural Language Processing." International Journal of Computer Sciences and Engineering 6, no. 1 (January 31, 2018): 161–67. http://dx.doi.org/10.26438/ijcse/v6i1.161167.

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6

Yilmaz, A. Egemen. "Natural Language Processing." International Journal of Systems and Service-Oriented Engineering 4, no. 1 (January 2014): 68–83. http://dx.doi.org/10.4018/ijssoe.2014010105.

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Анотація:
Requirement analysis is the very first and crucial step in the software development processes. On the other hand, as previously addressed by other researchers, it is the Achilles' heel of the whole process since the requirements lie on the problem space, whereas other software artifacts are on the solution space. Stating the requirements in a clear manner eases the following steps in the process as well as reducing the number of potential errors. In this paper, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.
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7

Weischedel, R., J. Carbonell, B. Grosz, W. Lehnert, M. Marcus, R. Perrault, and R. Wilensky. "Natural Language Processing." Annual Review of Computer Science 4, no. 1 (June 1990): 435–52. http://dx.doi.org/10.1146/annurev.cs.04.060190.002251.

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8

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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9

Patten, T., and P. Jacobs. "Natural-language processing." IEEE Expert 9, no. 1 (February 1994): 35. http://dx.doi.org/10.1109/64.295134.

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10

Rindflesch, Thomas C. "Natural Language Processing." Annual Review of Applied Linguistics 16 (March 1996): 70–85. http://dx.doi.org/10.1017/s0267190500001446.

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Анотація:
Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.
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Дисертації з теми "080107 Natural Language Processing"

1

Meyer, Christopher Henry. "On improving natural language processing through phrase-based and one-to-one syntactic algorithms." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1096.

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Matsubara, Shigeki. "Corpus-based Natural Language Processing." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10355.

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3

Smith, Sydney. "Approaches to Natural Language Processing." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1817.

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Анотація:
This paper explores topic modeling through the example text of Alice in Wonderland. It explores both singular value decomposition as well as non-­‐‑negative matrix factorization as methods for feature extraction. The paper goes on to explore methods for partially supervised implementation of topic modeling through introducing themes. A large portion of the paper also focuses on implementation of these techniques in python as well as visualizations of the results which use a combination of python, html and java script along with the d3 framework. The paper concludes by presenting a mixture of SVD, NMF and partially-­‐‑supervised NMF as a possible way to improve topic modeling.
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4

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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Анотація:
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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5

Chen, Joseph C. H. "Quantum computation and natural language processing." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=965581020.

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6

Knight, Sylvia Frances. "Natural language processing for aerospace documentation." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621395.

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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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Анотація:
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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8

Eriksson, Simon. "COMPARING NATURAL LANGUAGE PROCESSING TO STRUCTURED QUERY LANGUAGE ALGORITHMS." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163310.

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Анотація:
Using natural language processing to create Structured Query Language (SQL) queries has many benefi€ts in theory. Even though SQL is an expressive and powerful language it requires certain technical knowledge to use. An interface effectively utilizing natural language processing would instead allow the user to communicate with the SQL database as if they were communicating with another human being. In this paper I compare how two of the currently most advanced open source algorithms (TypeSQL and SyntaxSQL) in this €field can understandadvanced SQL. I show that SyntaxSQL is signi€cantly more accurate but makes some sacri€ces in execution time compared to TypeSQL.
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9

Kesarwani, Vaibhav. "Automatic Poetry Classification Using Natural Language Processing." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37309.

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Poetry, as a special form of literature, is crucial for computational linguistics. It has a high density of emotions, figures of speech, vividness, creativity, and ambiguity. Poetry poses a much greater challenge for the application of Natural Language Processing algorithms than any other literary genre. Our system establishes a computational model that classifies poems based on similarity features like rhyme, diction, and metaphor. For rhyme analysis, we investigate the methods used to classify poems based on rhyme patterns. First, the overview of different types of rhymes is given along with the detailed description of detecting rhyme type and sub-types by the application of a pronunciation dictionary on our poetry dataset. We achieve an accuracy of 96.51% in identifying rhymes in poetry by applying a phonetic similarity model. Then we achieve a rhyme quantification metric RhymeScore based on the matching phonetic transcription of each poem. We also develop an application for the visualization of this quantified RhymeScore as a scatter plot in 2 or 3 dimensions. For diction analysis, we investigate the methods used to classify poems based on diction. First the linguistic quantitative and semantic features that constitute diction are enumerated. Then we investigate the methodology used to compute these features from our poetry dataset. We also build a word embeddings model on our poetry dataset with 1.5 million words in 100 dimensions and do a comparative analysis with GloVe embeddings. Metaphor is a part of diction, but as it is a very complex topic in its own right, we address it as a stand-alone issue and develop several methods for it. Previous work on metaphor detection relies on either rule-based or statistical models, none of them applied to poetry. Our methods focus on metaphor detection in a poetry corpus, but we test on non-poetry data as well. We combine rule-based and statistical models (word embeddings) to develop a new classification system. Our first metaphor detection method achieves a precision of 0.759 and a recall of 0.804 in identifying one type of metaphor in poetry, by using a Support Vector Machine classifier with various types of features. Furthermore, our deep learning model based on a Convolutional Neural Network achieves a precision of 0.831 and a recall of 0.836 for the same task. We also develop an application for generic metaphor detection in any type of natural text.
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Pham, Son Bao Computer Science &amp Engineering Faculty of Engineering UNSW. "Incremental knowledge acquisition for natural language processing." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/26299.

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Анотація:
Linguistic patterns have been used widely in shallow methods to develop numerous NLP applications. Approaches for acquiring linguistic patterns can be broadly categorised into three groups: supervised learning, unsupervised learning and manual methods. In supervised learning approaches, a large annotated training corpus is required for the learning algorithms to achieve decent results. However, annotated corpora are expensive to obtain and usually available only for established tasks. Unsupervised learning approaches usually start with a few seed examples and gather some statistics based on a large unannotated corpus to detect new examples that are similar to the seed ones. Most of these approaches either populate lexicons for predefined patterns or learn new patterns for extracting general factual information; hence they are applicable to only a limited number of tasks. Manually creating linguistic patterns has the advantage of utilising an expert's knowledge to overcome the scarcity of annotated data. In tasks with no annotated data available, the manual way seems to be the only choice. One typical problem that occurs with manual approaches is that the combination of multiple patterns, possibly being used at different stages of processing, often causes unintended side effects. Existing approaches, however, do not focus on the practical problem of acquiring those patterns but rather on how to use linguistic patterns for processing text. A systematic way to support the process of manually acquiring linguistic patterns in an efficient manner is long overdue. This thesis presents KAFTIE, an incremental knowledge acquisition framework that strongly supports experts in creating linguistic patterns manually for various NLP tasks. KAFTIE addresses difficulties in manually constructing knowledge bases of linguistic patterns, or rules in general, often faced in existing approaches by: (1) offering a systematic way to create new patterns while ensuring they are consistent; (2) alleviating the difficulty in choosing the right level of generality when creating a new pattern; (3) suggesting how existing patterns can be modified to improve the knowledge base's performance; (4) making the effort in creating a new pattern, or modifying an existing pattern, independent of the knowledge base's size. KAFTIE, therefore, makes it possible for experts to efficiently build large knowledge bases for complex tasks. This thesis also presents the KAFDIS framework for discourse processing using new representation formalisms: the level-of-detail tree and the discourse structure graph.
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Книги з теми "080107 Natural Language Processing"

1

Natural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.

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2

Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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3

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

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4

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

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5

Kulkarni, Akshay, and Adarsha Shivananda. Natural Language Processing Recipes. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.

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Kulkarni, Akshay, Adarsha Shivananda, and Anoosh Kulkarni. Natural Language Processing Projects. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.

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Søgaard, Anders. Explainable Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02180-0.

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Tapsai, Chalermpol, Herwig Unger, and Phayung Meesad. Thai Natural Language Processing. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56235-9.

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Oflazer, Kemal, and Murat Saraçlar, eds. Turkish Natural Language Processing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7.

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Sharkey, Noel, ed. Connectionist Natural Language Processing. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2624-3.

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Частини книг з теми "080107 Natural Language Processing"

1

Brill, Eric. "Processing Natural Language without Natural Language Processing." In Computational Linguistics and Intelligent Text Processing, 360–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36456-0_37.

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2

Morgan, David P., and Christopher L. Scofield. "Natural Language Processing." In Neural Networks and Speech Processing, 245–88. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3950-6_8.

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Mizoguchi, Fumio. "Natural language processing." In Prolog and its Applications, 156–203. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-7144-9_4.

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Bhattacharjee, Joydeep. "Natural Language Processing." In Practical Machine Learning with Rust, 187–227. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5121-8_5.

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Lee, Raymond S. T. "Natural Language Processing." In Artificial Intelligence in Daily Life, 157–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7695-9_6.

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Arnold, Taylor, and Lauren Tilton. "Natural Language Processing." In Quantitative Methods in the Humanities and Social Sciences, 131–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20702-5_9.

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Linckels, Serge, and Christoph Meinel. "Natural Language Processing." In X.media.publishing, 61–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17743-9_4.

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Akerkar, Rajendra. "Natural Language Processing." In Artificial Intelligence for Business, 53–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97436-1_5.

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Chowdhary, K. R. "Natural Language Processing." In Fundamentals of Artificial Intelligence, 603–49. New Delhi: Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_19.

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Kumar, Rohit. "Natural Language Processing." In Machine Learning and Cognition in Enterprises, 65–73. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3069-5_5.

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Тези доповідей конференцій з теми "080107 Natural Language Processing"

1

Schmaltz, Allen. "On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight." In Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-0801.

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Hearst, Marti A. "Can Natural Language Processing Become Natural Language Coaching?" In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-1120.

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3

Gelbukh, A. "Natural language processing." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.79.

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"Natural language processing." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377321.

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"Natural language processing." In 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004. IEEE, 2004. http://dx.doi.org/10.1109/inmic.2004.1492945.

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Schank, Roger, and Alex Kass. "Natural language processing." In the 1987 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1987. http://dx.doi.org/10.3115/980304.980329.

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Trifan, Alexandru, Marilena Anghelus, and Rodica Constantinescu. "Natural language processing model compiling natural language into byte code." In 2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD). IEEE, 2017. http://dx.doi.org/10.1109/sped.2017.7990434.

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Ferrari, Alessio. "Natural language requirements processing." In ICSE '18: 40th International Conference on Software Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183440.3183467.

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Weischedel, Ralph. "Adaptive natural language processing." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1991. http://dx.doi.org/10.3115/112405.1138642.

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Weischedel, Ralph. "Adaptive natural language processing." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1990. http://dx.doi.org/10.3115/116580.1138590.

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Звіти організацій з теми "080107 Natural Language Processing"

1

Steedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada290396.

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2

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

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3

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

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4

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

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5

Anderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada188112.

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Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada258837.

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Neal, Jeannette G., Elissa L. Feit, Douglas J. Funke, and Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada263301.

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Lehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273538.

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Wallace, Byron C. Sociolinguistically Informed Natural Language Processing: Automating Irony Detection. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada623456.

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Stickland, Michael G., Gregory N. Conrad, and Shelley M. Eaton. Natural language processing-based COTS software and related technologies survey. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/917479.

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