Relevant bibliographies by topics / Natural language processing (Computer science) Compuational linguistics

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

Academic literature on the topic 'Natural language processing (Computer science) Compuational linguistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Natural language processing (Computer science) Compuational linguistics"

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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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Kim, Jin-Dong. "Biomedical Natural Language Processing." Computational Linguistics 43, no. 1 (April 2017): 265–67. http://dx.doi.org/10.1162/coli_r_00281.

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Dale, Robert, Hermann Moisl, and Harold Somers. "Handbook of Natural Language Processing." Computational Linguistics 27, no. 4 (December 2001): 602–3. http://dx.doi.org/10.1162/coli.2000.27.4.602.

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GAIZAUSKAS, R., P. J. RODGERS, and K. HUMPHREYS. "Visual Tools for Natural Language Processing." Journal of Visual Languages & Computing 12, no. 4 (August 2001): 375–412. http://dx.doi.org/10.1006/jvlc.2000.0203.

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Bird, Steven. "Natural Language Processing and Linguistic Fieldwork." Computational Linguistics 35, no. 3 (September 2009): 469–74. http://dx.doi.org/10.1162/coli.35.3.469.

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Louis, Annie. "Natural Language Processing for Social Media." Computational Linguistics 42, no. 4 (December 2016): 833–36. http://dx.doi.org/10.1162/coli_r_00270.

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Duh, Kevin. "Bayesian Analysis in Natural Language Processing." Computational Linguistics 44, no. 1 (March 2018): 187–89. http://dx.doi.org/10.1162/coli_r_00310.

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Armstrong, Susan, Kenneth Church, Pierre Isabelle, Sandra Manzi, Evelyne Tzoukermann, and David Yarowsky. "Natural Language Processing Using Very Large Corpora." Computational Linguistics 26, no. 2 (June 2000): 294. http://dx.doi.org/10.1162/coli.2000.26.2.294a.

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Liu, Yang, and Meng Zhang. "Neural Network Methods for Natural Language Processing." Computational Linguistics 44, no. 1 (March 2018): 193–95. http://dx.doi.org/10.1162/coli_r_00312.

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Amaral, Luiz, Detmar Meurers, and Ramon Ziai. "Analyzing learner language: towards a flexible natural language processing architecture for intelligent language tutors." Computer Assisted Language Learning 24, no. 1 (January 25, 2011): 1–16. http://dx.doi.org/10.1080/09588221.2010.520674.

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Dissertations / Theses on the topic "Natural language processing (Computer science) Compuational linguistics"

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Vaillette, Nathan. "Logical specification of finite-state transductions for natural language processing." Columbus, Ohio : Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1072058657.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xv, 253 p.; also includes graphics. Includes abstract and vita. Advisor: Chris Brew, Dept. of Linguistics. Includes bibliographical references (p. 245-253).
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Jarmasz, Mario. ""Roget's Thesaurus" as a lexical resource for natural language processing." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26493.

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This dissertation presents an implementation of an electronic lexical knowledge base that uses the 1987 Penguin edition of Roget's Thesaurus as the source for its lexical material---the first implementation of a computerized Roget's to use an entire current edition. It explains the steps necessary for taking a machine-readable file and transforming it into a tractable system. Roget's organization is studied in detail and contrasted with WordNet's. We show two applications of the computerized Thesaurus: computing semantic similarity between words and phrases, and building lexical chains in a text. The experiments are performed using well-known benchmarks and the results are compared to those of other systems that use Roget's, WordNet and statistical techniques. Roget's has turned out to be an excellent resource for measuring semantic similarity; lexical chains are easily built but more difficult to evaluate. We also explain ways in which Roget's Thesaurus and WordNet can be combined.
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Berman, Lucy. "Lewisian Properties and Natural Language Processing: Computational Linguistics from a Philosophical Perspective." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2200.

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Nothing seems more obvious than that our words have meaning. When people speak to each other, they exchange information through the use of a particular set of words. The words they say to each other, moreover, are about something. Yet this relation of “aboutness,” known as “reference,” is not quite as simple as it appears. In this thesis I will present two opposing arguments about the nature of our words and how they relate to the things around us. First, I will present Hilary Putnam’s argument, in which he examines the indeterminacy of reference, forcing us to conclude that we must abandon metaphysical realism. While Putnam considers his argument to be a refutation of non-epistemicism, David Lewis takes it to be a reductio, claiming Putnam’s conclusion is incredible. I will present Lewis’s response to Putnam, in which he accepts the challenge of demonstrating how Putnam’s argument fails and rescuing us from the abandonment of realism. In order to explain the determinacy of reference, Lewis introduces the concept of “natural properties.” In the final chapter of this thesis, I will propose another use for Lewisian properties. Namely, that of helping to minimize the gap between natural language processing and human communication.
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Keller, Thomas Anderson. "Comparison and Fine-Grained Analysis of Sequence Encoders for Natural Language Processing." Thesis, University of California, San Diego, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10599339.

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Most machine learning algorithms require a fixed length input to be able to perform commonly desired tasks such as classification, clustering, and regression. For natural language processing, the inherently unbounded and recursive nature of the input poses a unique challenge when deriving such fixed length representations. Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning, the same cannot be said for sequences of words in sentences, paragraphs, or documents. In this work, we study the encoders commonly used to generate fixed length representations of natural language sequences, and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering. Additionally, we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks. Ultimately, we show that the setting in which the encoders are trained, and the corpus used for training, have a greater influence of the final learned representation than the underlying sequence encoders themselves.

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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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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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Schäfer, Ulrich. "Integrating deep and shallow natural language processing components : representations and hybrid architectures /." Saarbrücken : German Reseach Center for Artificial Intelligence : Saarland University, Dept. of Computational Linguistics and Phonetics, 2007. http://www.loc.gov/catdir/toc/fy1001/2008384333.html.

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Mahamood, Saad Ali. "Generating affective natural language for parents of neonatal infants." Thesis, University of Aberdeen, 2010. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=158569.

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The thesis presented here describes original research in the field of Natural Language Generation (NLG). NLG is the subfield of artificial intelligence that is concerned with the automatic production of documents from underlying data. This thesis in particular focuses on developing new and novel methods for generating text that takes into consideration the recipient’s level of stress as a factor to adapt the resultant textural output. This consideration of taking the recipient level of stress was particularly salient due to the domain that this research was conducted under; providing information for parents of pre-term infants during neonatal intensive care (NICU). A highly technical and stressful environment for parents where emotional sensitivity must be shown for the nature of information presented. We have investigated the emotional and informational needs of these parents through an extensive past literature review and two separate research studies with former and current NICU parents. The NLG system built for this research was called BabyTalk Family (BT-Family). A system that can produce a textual summary of medical events that has occurred for a baby in NICU in last twenty-four hours for parents. The novelty of this system is that is capable of estimating the level of stress of the recipient and by using several affective NLG strategies it is able to tailor it’s output for a stressed audience. Unlike traditional NLG systems where the output would remain unchanged regardless of emotional state of the recipient. The key innovation in this system was the integration of several affective strategies in the Document Planner for tailoring textual output for stress recipients. BT-Family’s output was evaluated with thirteen parents that previously had baby in neonatal care. We developed a methodology for an evaluation that involved a direct comparison between stressed and unstressed text for the same given medical scenario for variables such as preference, understandability, helpfulness, and emotional appropriateness. The results, obtained showed the parents overwhelming preferred the stressed text for all of the variables measured.
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Kozlowski, Raymond. "Uniform multilingual sentence generation using flexible lexico-grammatical resources." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.93 Mb., 213 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3200536.

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Carpuat, Marine Jacinthe. "Word sense alignment using bilingual corpora /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20CARPUA.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 43-44). Also available in electronic version. Access restricted to campus users.
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Petersen, Sarah E. "Natural language processing tools for reading level assessment and text simplication for bilingual education /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6906.

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Books on the topic "Natural language processing (Computer science) Compuational linguistics"

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

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Sharkey, Noel. Connectionist Natural Language Processing: Readings from Connection Science. Dordrecht: Springer Netherlands, 1992.

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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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Clark, Alexander. The handbook of computational linguistics and natural language processing. Malden, Ma: Wiley-Blackwell, 2010.

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Clark, Alexander. The handbook of computational linguistics and natural language processing. Malden, Ma: Wiley-Blackwell, 2010.

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Strzalkowski, Tomek. Reversible Grammar in Natural Language Processing. Boston, MA: Springer US, 1994.

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Language equations. New York: Springer, 1998.

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Chiarcos, Christian, and Sebastian Hellmann. Linked data in linguistics: Representing and connecting language data and language metadata. Edited by Nordoff Sebastian. Heidelberg: Springer, 2012.

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Book chapters on the topic "Natural language processing (Computer science) Compuational linguistics"

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Choi, Youngseok, Jungsuk Oh, and Jinsoo Park. "A Novel Approach to Managing the Dynamic Nature of Semantic Relatedness." In Natural Language Processing, 1085–114. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch052.

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This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm.
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Glad Shiya V., Belsini, and Sharmila K. "Language Processing and Python." In Advances in Computational Intelligence and Robotics, 93–119. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7728-8.ch006.

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Natural language processing is the communication between the humans and the computers. It is the field of computer science which incorporates artificial intelligence and linguistics where machine learning algorithms are used to analyze and process the enormous variety of data. This chapter delivers the fundamental concepts of language processing in Python such as text and word operations. It also gives the details about the preference of Python language for language processing and its advantages. It specifies the basic concept of variables, list, operators, looping statements in Python and explains how it can be implemented in language processing. It also specifies how a structured program can be written using Python, categorizing and tagging of words, how an information can be extracted from a text, syntactic and semantic analysis, and NLP applications. It also concentrates some of the research applications where NLP is applied and the challenges of NLP processing in the real-time area of applications.
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Paul, Saptarshi. "Understanding Aviation English: Challenges and Opportunities in NLP Applications for Indian Languages." In Air Traffic Management and Control [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99612.

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English is a language that is understood, spoken and used by citizens of a diverse array of countries. The speakers include both native and non-native speakers of English. NLP or Natural Language Processing on the other hand is a branch of computer science that deals with one of the most challenging aspect that a machine can process: dealing with Natural Languages. Natural languages which have evolved over centuries are complete, diverse and highly complex and thus are challenging for a computer system to understand and process. MT or Machine Translation is a more specific part of NLP that translates one natural language to another (English being one of the major researched and sought after languages among them). Though research in the field of NLP and MT has come a long way and many efficient translators are available, still Translation and other NLP applications in specialized domains such as aeronautics are still today a challenge for NLP researchers and developers to achieve. NLP applications are often used in education of English Language, and are therefore a continuous process for Non-Native speakers of English. Non-native English speakers take help of various NLP tools such as E-Dictionary, MT applications and others to better understand the English language and thus learn it better and faster. Aviation English poses a challenge to MT systems and understanding it as a whole requires specialized handling as it has own phonetic pronunciations and terminologies and constituent Out-Of-Vocabulary words. Dealing with Aviation English calls for teaming up of experts from Applied Linguistics, NLP and AI. As a result it becomes a cross-research discipline that covers situations that demand real time use of proper language, e.g. ATC communications. This Paper aims to discuss most recent research methodologies that deals with the Aviation English and reviews the problems posed by it. Being a specialized and structured form of English, the problems are faced by both native and non-native speakers of English Language. Discussion is carried out in the relevant and recent advances of methods in dealing with aviation English language challenges from both, the Human (ICAO/DGCA/AAI) as well as NLP angle. Lastly we have a look at how these challenges are linked to scope for development of applied technologies. Research in experiential Aviation English situations deals with both English for Specific Purposes - ESP (Aeronautics in our case) as well as situations in English as a Foreign Language i.e. EFL (English-Indian language pair).
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Conference papers on the topic "Natural language processing (Computer science) Compuational linguistics"

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"Systematic Improvement of User Engagement with Academic Titles Using Computational Linguistics." In InSITE 2019: Informing Science + IT Education Conferences: Jerusalem. Informing Science Institute, 2019. http://dx.doi.org/10.28945/4338.

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Aim/Purpose: This paper describes a novel approach to systematically improve information interactions based solely on its wording. Background: Providing users with information in a form and format that maximizes its effectiveness is a research ‎question of critical importance. Given the growing competition for ‎users’ attention and interest, it is agreed that digital content must engage. However, there are no clear methods or ‎frameworks for evaluation, optimization and creation of such engaging content. Methodology: Following an interdisciplinary literature review, we recognized three key attributes of words that drive user engagement: (1) Novelty (2) Familiarity (3) Emotionality. Based on these attributes, we developed a model to systematically improve a ‎given content using computational linguistics, natural language processing (NLP) and text analysis (word frequency, sentiment analysis and lexical substitution). We conducted a pilot study (n=216) in which the model was used to ‎formalize evaluation and optimization of academic titles. A between-group design (A/B testing) was used to compare responses to the ‎original and modified (treatment) titles. Data was collected for selection and evaluation (User Engagement Scale). Contribution: The pilot results suggest that user engagement‎ with digital information is ‎fostered by, and perhaps dependent upon, the wording being used. They also provide empirical support that engaging content can be systematically evaluated and produced. Findings: The preliminary results show that the modified (treatment) titles had significantly higher scores for information use and user engagement (selection and evaluation). Recommendations for Practitioners: We ‎propose that computational linguistics is a useful approach for optimizing information interactions. The ‎empirically based insights can inform the development of digital content strategies, ‎thereby improving the ‎success of information interactions. ‎ Recommendations for Researchers: By understanding and operationalizing ‎content strategy and engagement, we can ‎begin to ‎focus efforts on designing interfaces which ‎engage users with features ‎‎‎appropriate to the task and context of their interactions. This study will benefit the ‎information science field by ‎enabling researchers ‎and practitioners ‎alike to ‎understand the dynamic relationship ‎between users, computer applications and ‎tasks, ‎how to ‎assess whether ‎engagement is taking place and how to design ‎interfaces that ‎engage ‎users.‎ Impact on Society: This research can be used as an important starting point for ‎understanding ‎the phenomenon of digital ‎information interactions and the factors that promote ‎and facilitates them. It can also aid in the ‎‎development of a broad framework for systematic evaluation, ‎optimization, and creation of effective digital ‎content. ‎ Future Research: Moving forward, the validity, reliability and generalizability of ‎our model should be tested in various ‎contexts. In future research, we propose to include additional linguistic factors and ‎develop more ‎sophisticated interaction measures. ‎
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