Natural Language Annotation for Machine Learning
by James Pustejovsky and Amber Stubbs · 14 Oct 2012 · 502pp · 107,510 words
while we were writing, especially Marc Verhagen, Lotus Goldberg, Jessica Moszkowicz, and Alex Plotnick. This book could not exist without everyone in the linguistics and computational linguistics communities who have created corpora and annotations, and, more importantly, shared their experiences with the rest of the research community. James Adds: I would like
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other media. However, while computers are excellent at delivering this information to interested users, they are much less adept at understanding language itself. Theoretical and computational linguistics are focused on unraveling the deeper nature of language and capturing the computational properties of linguistic structures. Human language technologies (HLTs) attempt to adopt these
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Is Natural Language Processing? Natural Language Processing (NLP) is a field of computer science and engineering that has developed from the study of language and computational linguistics within the field of Artificial Intelligence. The goals of NLP are to design and build applications that facilitate human interaction with machines and other devices
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ontology, such as that shown in Figure 1-9. Note The word ontology has its roots in philosophy, but ontologies also have a place in computational linguistics, where they are used to create categorized hierarchies that group similar concepts and objects. By assigning words semantic types in an ontology, we can create
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all over again, and revisions will typically bring improved performance. Summary In this chapter, we have provided an overview of the history of corpus and computational linguistics, and the general methodology for creating an annotated corpus. Specifically, we have covered the following points: Natural language annotation is an important step in the
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course, Google usually provide good starting places, those sources might not have the latest information on annotation projects, particularly because the primary publishing grounds in computational linguistics are conferences and their related workshops. In the following sections we’ll give you some pointers to organizations and workshops that may prove useful. Language
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of some of the bigger conferences that examine annotation and corpora, as well as some organizations that are interested in the same topic: Association for Computational Linguistics (ACL) Institute of Electrical and Electronics Engineers (IEEE) Language Resources and Evaluation Conference (LREC) European Language Resources Association (ELRA) Conference on
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Computational Linguistics (COLING) American Medical Informatics Association (AMIA) The LINGUIST List is not an organization that sponsors conferences and workshops itself, but it does keep an excellent
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. Some workshops that you may want to look into are: SemEval This is a workshop held every three years as part of the Association for Computational Linguistics. It involves a variety of challenges including word sense disambiguation, temporal and spatial reasoning, and Machine Translation. Conference on Natural Language Learning (CoNLL) Shared Task
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This is a yearly NLP challenge held as part of the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics. Each year a new NLP task is chosen for the challenge. Past challenges include uncertainty detection, extracting syntactic and semantic dependencies, and multilingual processing. i2b2
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problem of sparse data. That is, the probabilities of observed rare events are overestimated, and the probabilities of unobserved rare events are underestimated. Researchers in computational linguistics have found ways to get around this problem to a certain extent, and we will return to this issue when we discuss ML algorithms in
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://www.tei-c.org/Guidelines/P5/. 2012: LAF and the TEI Guidelines are still being updated and improved to reflect progress made in corpus and computational linguistics. Annotation specification standards In addition to helping create standards for annotation formats, ISO is working on developing standards for specific annotation tasks. We mentioned ISO
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than knowing the language of the text, we recommend first checking to see if there are any local colleges or universities that have linguistics or computational linguistics programs. Depending on the task, it might not even be necessary to require that an annotator be a linguist, but if your task involves
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set of texts, it’s time to evaluate the IAA scores. While there are many different ways to determine agreement, the two most common in computational linguistics are Cohen’s Kappa and Fleiss’s Kappa. Cohen’s Kappa is used if you have only two annotators annotating a document, while Fleiss’s
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Nobel Prize-winning economist Herbert Simon: Learning is any process by which a system improves its performance from experience. For areas in language technology and computational linguistics, the most important topics for learning include the following: Assigning categories to words (part-of-speech [POS] tagging) Assigning topics to articles, emails, or web
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or sequence of tokens (the condition), and then applying some sort of tag or performing some sort of labeling (the action). In many areas of computational linguistics, in fact, rule-based systems will perform as well as if not better than statistically trained ML algorithms. Rules are connections between the condition and
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handy way for someone to tell at a glance how your algorithm is performing in general. This is why F-measure is commonly reported in computational linguistics papers: not because it’s inherently useful to people trying to find ways to improve their algorithm, but because it’s easier on the reader
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Research With the stated goals in mind, the working group began looking into research that was related to their project. Specifically, they looked at existing computational linguistics research and projects to determine what aspects of their goals had already been implemented in some form, and then they looked at more theoretical treatments
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ML side, particularly for corpora that are large, but perhaps not fully annotated. Almost all of the techniques that attempt to handle Big Data in computational linguistics (and Artificial Intelligence in general) approach the phenomenon as an opportunity rather than a problem. With so much information, so this reasoning goes, is there
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built by others. And Finally... In this chapter our goal was to show you the role that annotation is playing in cutting-edge developments in computational linguistics and machine learning. We pointed out how the different components of the MATTER development cycle are being experimented with and improved, including new ways to
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data, and stronger communities for resource sharing and collaboration. Because of a lack of understanding of the role that annotation plays in the development of computational linguistics systems, there is always some discussion that the role of annotation is outdated; that with enough data, accurate clusters can be found without the need
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. 1984. “Towards a General Theory of Action and Time.” Artificial Intelligence 23:123–154. Artstein, Ron, and Massimo Poesio. December 2008. “Inter-coder agreement for computational linguistics.” Computational Linguistics 34(4):555–596. Atkins, Sue, and N. Ostler. 1992. “Predictable Meaning Shift: Some Linguistic Properties of Lexical Implication Rules.” In Lexical Semantics and Commonsense
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de Gruyter: 803–821. Bayerl, Petra Saskia, and Karsten Ingmar Paul. December 2011. “What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation.” Computational Linguistics 37(4):699–725. Belnap, Nuel. 1992. “Branching Space-Time.” Synthese 92:385–434. Biber, Douglas. 1993. “Representativeness in Corpus Design.” Literary and Linguistic Computing
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Australasian Language Technology Workshop. Brill, Eric. December 1995. “Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging.” Computational Linguistics 21(4):543–565. Carlson, Andrew, Justin Betteridge, Richard C. Wang, Estevam R. Hruschka Jr., and Tom M. Mitchell. 2010. “Coupled Semi-Supervised Learning for
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. 2007. “First-Order Probabilistic Models for Coreference Resolution.” In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL). Derczynski, Leon, and Robert Gaizauskas. 2010. “USFD2: Annotating Temporal Expressions and TLINKs for TempEval-2.” In Proceedings of the 5th International Workshop on
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Workshop on Temporal and Spatial Reasoning, Toulouse, France. Fort, Karën, Gilles Adda, and K. Bretonnel Cohen. 2011. “Amazon Mechanical Turk: Gold Mine or Coal Mine?” Computational Linguistics 37(2):413–420. Francis, W.N., and H. Kucera. 1964 (Revised 1971 and 1979). Brown Corpus Manual. Available online: http://khnt.aksis.uib.no
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.). Cambridge, UK: Cambridge University Press. Marcus, Mitchell P., Beatrice Santorini, and Mary Ann Marcinkiewicz. 1994. “Building a Large Annotated Corpus of English: The Penn Treebank.” Computational Linguistics 19(2):313–330. McCallum, D. Freitag, and F. Pereira. 2000. “Maximum entropy Markov models for information extraction and segmentation.” In Proceedings of ICML 2000
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–221. Ng, Vincent, and Claire Cardie. 2002. “Improving Machine Learning Approaches to Coreference Resolution.” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Ng, Vincent, and Claire Cardie. 2003. “Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms.” In Proceedings of the 2003 Conference on Empirical Methods in Natural
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Language Processing (EMNLP-2003), Association for Computational Linguistics. Nigam, Kamal, John Lafferty, and Andrew McCallum. 1999. “Using maximum entropy for text classification.” In Proceedings of the IJCAI-99 Workshop on Machine Learning for
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the context of natural language processing. [SICS Report] Palmer, Martha, Dan Gildea, and Paul Kingsbury. 2005. “The Proposition Bank: A Corpus Annotated with Semantic Roles.” Computational Linguistics Journal 31(1):71–106. Panchenko, Alexander, Sergey Adeykin, Alexey Romanov, and Pavel Romanov. 2012. “Extraction of Semantic Relations between Concepts with KNN Algorithms on
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Lee. 2004. “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.” In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL ’04), Stroudsburg, PA, Article 271. Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. “Thumbs Up? Sentiment Classification Using Machine Learning Techniques.” In Proceedings of
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the ACL-02 Conference on Empirical Methods in Natural Language Processing (vol. 10, EMNLP ’02, pp. 79–86), Stroudsburg, PA; Association for Computational Linguistics. Parsons, Terence. 1990. Events in the Semantics of English: A Study of Subatomic Semantics. Cambridge, MA: The MIT Press. Pomikálek, Jan, Miloš Jakubíček, and Pavel
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ACL-COLING, Geneva. Pustejovsky, James, and Amber Stubbs. 2011. “Increasing Informativeness in Temporal Annotation.” In 2011 Proceedings of the Linguistic Annotation Workshop V, Association of Computational Linguistics, Portland, OR, July 23–24, 2011. Quirk, Randolph, Sidney Greenbaum, Geoffrey Leech, and Jan Svartik.1985. A Comprehensive Grammar of the English Language. London and
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and Demonstration Sessions. Verhagen, Marc, and James Pustejovsky. 2008. “Temporal processing with the TARSQI Toolkit.” In Proceedings of COLING ’08, the 22nd International Conference on Computational Linguistics: Demonstration Papers. Verhagen, Marc, and James Pustejovsky. 2012. “The TARSQI Toolkit.” In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12
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Language Resources and Evaluation (LREC’12), Istanbul, Turkey. Fort, Karën, Gilles Adda, and K. Bretonnel Cohen. 2011. “Amazon Mechanical Turk: Gold Mine or Coal Mine?” Computational Linguistics 37(2):413–420. Kittur, E.H. Chi, and B. Suh. 2008. “Crowdsourcing user studies with Mechanical Turk.” In Proceedings of CHI ’08: The 26th
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the Corpus ARDA workshop, and the TimeML, Annotation: TimeML arity, Some Example Models and Specs Artificial Intelligence (AI), Language Data and Machine Learning Association for Computational Linguistics (ACL), Organizations and Conferences ATIS, as source for Penn TreeBank, What Is a Corpus? automatic annotation, Automatic Annotation: Generating TimeML–Cross-Document Analysis Automatic Content
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condition-action pair, Defining Our Learning Task conditional probability, Joint Probability Distributions Conditional Random Field models (CRF), Structured Pattern Induction, Sequence Induction Algorithms Conference on Computational Linguistics (COLING), Organizations and Conferences Conference on Natural Language Learning (CoNLL) Shared Task (Special Interest Group on Natural Language Learning of the Association for
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Computational Linguistics), NLP Challenges confusion matrix, Cohen’s Kappa (κ), Confusion Matrices consuming tags, Annotate with the Specification corpus analytics, Corpus Analytics–Language Models, Basic Probability for
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Roles semantic typing and ontology, Kinds of Annotation semantic value, Kinds of Annotation SemEval 2007 and 2010, TimeML Challenges: TempEval-2 SemEval challenge (Association for Computational Linguistics), NLP Challenges semi-supervised learning (SSL), Semi-Supervised Learning–Semi-Supervised Learning (see SSL (semi-supervised learning)) Sentiment Quiz (GWAP), Games with a Purpose (GWAP
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), Some Example Models and Specs Z Zipf’s Law, Zipf’s Law About the Authors James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. His main areas of interest include: lexical meaning, computational semantics, temporal and spatial reasoning, and corpus linguistics
Machine Translation
by Thierry Poibeau · 14 Sep 2017 · 174pp · 56,405 words
and Ambiguity Linguists as well as computer scientists have been interested ever since the creation of computers in natural language processing, a field also called computational linguistics. Natural language processing is difficult because, by default, computers do not have any knowledge of what a language is. It is thus necessary to specify
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productive for machine translation, especially in the Anglo-American world. New groups nevertheless emerged in Europe and other countries. On the other hand, research in computational linguistics was blooming during the same period for speech as well as for written text: the 1960s and 1970s saw major developments in parsing (automatic syntactic
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level of demand for automatic translation over the web has also had the effect of reinstating machine translation at the heart of the field of computational linguistics, after several decades in purgatory. A new approach based on deep learning is also completely revolutionizing the field since the mid-2010s. We now need
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that was emerging in the United States. In fact, the center closed a few years later and some researchers, such as Maurice Gross, turned to computational linguistics, stressing the need to first develop rich linguistic resources that offer a broad and systematic description of language. The Grenoble center has survived to the
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information, developed by Colmerauer (this formalism can be seen as a precursor of the Prolog programming language that has been since then very popular in computational linguistics and more generally in artificial intelligence) and, above all, probably the most well-known automatic translation system: TAUM-Météo (later referred to simply as Météo
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A. Gale and Kenneth W. Church (1993). “A program for aligning sentences in bilingual corpora.” Journal of Computational Linguistics 19 (1): 75–102. Martin Kay and Martin Röscheisen (1993). “Text-translation alignment.” Journal of Computational Linguistics 19 (1): 121–142. Makoto Nagao (1984). “A framework of a mechanical translation between Japanese and English by
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, Amsterdam. Eiichiro Sumita and Hitoshi Iida (1991). “Experiments and prospects of example-based machine translation.” Proceedings of the Twenty-Ninth Conference of the Association for Computational Linguistics, 185–192. Berkeley, CA. Thomas R. Green (1979). “The necessity of syntax markers: Two experiments with artificial languages.” Verbal Learning and Verbal Behavior 18: 481
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Pietra, Frederick Jelinek, Robert Mercer, and Paul Roossin (1988). “A statistical approach to language translation.” In Proceedings of the Twelfth Conference on Computational Linguistics, Vol. 1, 71–76. Association for Computational Linguistics, Stroudsburg, PA. http://dx.doi.org/10.3115/991635.991651/. Peter F. Brown, John Cocke, Stephen A. Della Pietra, Vincent J. Della
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Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, and Paul S. Roossin (1990). “A statistical approach to machine translation.” Computational Linguistics 16 (2): 79–85. Peter F. Brown, Vincent J. Della Pietra, Stephen A. Della Pietra, and Robert L. Mercer (1993). “The mathematics of statistical machine
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translation: Parameter estimation.” Computational Linguistics 19 (2): 263–311. Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016). Deep Learning. Cambridge, MA: MIT Press. Yonghui Wu, et al. (2016). “Google's
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, Salim Roukos, Todd Ward, and Wei-Jing Zhu (2002). “BLEU: A method for automatic evaluation of machine translation.” Fortieth Annual Meeting of the Association for Computational Linguistics, 311–318. Philadelphia. George Doddington (2002). “Automatic evaluation of machine translation quality using n-gram cooccurrence statistics.” Proceedings of the Human Language Technology Conference, 128
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Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization at the Forty-Third Annual Meeting of the Association of Computational Linguistics. Ann Arbor, MI. Martin Kay (2013). “Putting linguistics back into computational linguistics.” Conference given at the Ecole normale supérieure, Paris. http://savoirs.ens.fr/expose.php?id=1291/. Philipp Koehn, Alexandra Birch
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, 229 Complexity (linguistic), 18, 23, 182, 195, 255 Compound words, 15, 23, 33, 46, 164–165, 214, 261 Comprehension evaluation. See Evaluation measure and test Computational linguistics, 15, 36, 37, 68, 82–84 Computation time, 54, 149, 155, 170, Computer documentation, 119 Confidential data 230–231. See also Intelligence services Connected objects
The Cultural Logic of Computation
by David Golumbia · 31 Mar 2009 · 268pp · 109,447 words
is to say, a structure that is logically identical to (and often actually is) a computer program; as John Goldsmith, a leading practitioner of both computational linguistics (CL) and mainstream linguistics, has recently put it, “generative grammar is, more than it is anything else, a plea for the case that an insightful
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applied to them from the early days of Chomsky’s logic papers. Textbooks like Models of Computation and Formal Languages (Taylor 1998) and Foundations of Computational Linguistics (Hausser 2001) take such formal objects as obvious models for human language and then proceed to examine how much of human language can be understood
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. Perhaps because language per se is a much more objective part of the social world than is the abstraction called “thinking,” however, the history of computational linguistics reveals a particular dynamism with regard to the data it takes as its object— exaggerated claims, that is, are frequently met with material tests that
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not at all a new or technological one, but rather one of the oldest constitutive questions of culture and philosophy. Cryptography and the History of Computational Linguistics Chomsky’s CFG papers from the 1950s served provocatively ambivalent institutional functions. By putting human languages on the same continuum as formal languages, Chomsky underwrote
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although they are joined intellectually, are often pursued with apparent independence from each other—yet at the same time, the mere presence of the phrase “computational linguistics” in a title is often not at all enough to distinguish which program the researcher has in mind. SHRDLU and the State of the Art
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in Computational Linguistics The two faces of CL and NLP in its strong mode are either (1) to make computers use language in a fully human fashion, generally
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operates without regard to meaning, a project that has split linguistics itself as a discipline and that is still found in the generative grammar and computational linguistics projects (Chapter 4); in the installation of elaborate software programs that provide the owners of capital with heavily processed, concentrated, and statistical views of the
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Faculty of Language: What Is It, Who Has It, and How Did It Evolve?” Science 298 (November 22), 1569–1579. Hausser, Roland. 2001. Foundations of Computational Linguistics: HumanComputer Communication in Natural Language. Second edition. New York: Springer-Verlag. Hayles, N. Katherine. 1999. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and
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, 13, 149, 196 Cognitive science, 31–32, 49, 53–54, 60–61, 70–72, 115, 191 Index Colonialism, 120, 145–148, 153–154, 156, 203 Computational Linguistics (CL), 39, 46–47, 84–105, 189 Computational Theory of Mind (CTM), 63 Computer evangelism. See Evangelism (computer) Computer revolution, 121, 123, 130, 152, 182
Natural language processing with Python
by Steven Bird, Ewan Klein and Edward Loper · 15 Dec 2009 · 504pp · 89,238 words
introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is intensely practical, containing hundreds of fully worked examples
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from humanities computing and corpus linguistics through to computer science and artificial intelligence. (To many people in academia, NLP is known by the name of “Computational Linguistics.”) This book is intended for a diverse range of people who want to learn how to write programs that analyze written language, regardless of previous
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-order and equational logic, used to support inference in language processing. Natural Language Toolkit (NLTK) NLTK was originally created in 2001 as part of a computational linguistics course in the Department of Computer and Information Science at the University of Pennsylvania. Since then it has been developed and expanded with the help
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James Martin (2008) Speech and Language Processing (second edition), Prentice Hall. • Mitkov, Ruslan (ed., 2002) The Oxford Handbook of Computational Linguistics. Oxford University Press. (second edition expected in 2010). The Association for Computational Linguistics is the international organization that represents the field of NLP. The ACL website hosts many useful resources, including: information about
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by Generalized Phrase Structure Grammar (GPSG; [Gazdar et al., 1985]), particularly in the use of features with complex values. Coming more from the perspective of computational linguistics, (Kay, 1985) proposed that functional aspects of language could be captured by unification of attribute-value structures, and a similar approach was elaborated by (Grosz
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Graecae (TLG, 1999), Child Language Data Exchange System (CHILDES) (MacWhinney, 1995), and TIMIT (Garofolo et al., 1986). Two special interest groups of the Association for Computational Linguistics that organize regular workshops with published proceedings are SIGWAC, which promotes the use of the Web as a corpus and has sponsored the CLEANEVAL task
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Klavans and Philip Resnik, editors, The Balancing Act: Combining Symbolic and Statistical Approaches to Language. MIT Press, 1996. [Abney, 2008] Steven Abney. Semisupervised Learning for Computational Linguistics. Chapman and Hall, 2008. [Agirre and Edmonds, 2007] Eneko Agirre and Philip Edmonds. Word Sense Disambiguation: Algorithms and Applications. Springer, 2007. [Alpaydin, 2004] Ethem Alpaydin
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databases—an introduction. Journal of Natural Language Engineering, 1:29–81, 1995. [Artstein and Poesio, 2008] Ron Artstein and Massimo Poesio. Inter-coder agreement for computational linguistics. Computational Linguistics, pages 555–596, 2008. [Baayen, 2008] Harald Baayen. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge University Press, 2008. 449 [Bachenko and
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and American English. Lingua 118: 254–59, 2008. [Budanitsky and Hirst, 2006] Alexander Budanitsky and Graeme Hirst. Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics, 32:13–48, 2006. [Burton-Roberts, 1997] Noel Burton-Roberts. Analysing Sentences. Longman, 1997. [Buseman et al., 1996] Alan Buseman, Karen Buseman, and Rod Early
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the First International Workshop on Natural Language Understanding and Logic Programming. [Kiss and Strunk, 2006] Tibor Kiss and Jan Strunk. Unsupervised multilingual sentence boundary detection. Computational Linguistics, 32: 485–525, 2006. [Kiusalaas, 2005] Jaan Kiusalaas. Numerical Methods in Engineering with Python. Cambridge University Press, 2005. [Klein and Manning, 2003] Dan Klein and
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similarity. Language and Cognitive Processes, 6:1–28, 1998. [Mitkov, 2002a] Ruslan Mitkov. Anaphora Resolution. Longman, 2002. [Mitkov, 2002b] Ruslan Mitkov, editor. Oxford Handbook of Computational Linguistics. Oxford University Press, 2002. [Müller, 2002] Stefan Müller. Complex Predicates: Verbal Complexes, Resultative Constructions, and Particle Verbs in German. Number 13 in Studies in Constraint
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. CSLI Publications, Stanford, CA, 2003. [Pevzner and Hearst, 2002] L. Pevzner and M. Hearst. A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics, 28:19–36, 2002. [Pullum, 2005] Geoffrey K. Pullum. Fossilized prejudices about “however”, 2005. [Radford, 1988] Andrew Radford. Transformational Grammar: An Introduction. Cambridge University Press
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α-conversion, 389 α-equivalents, 389 β-reduction, 388 λ (lambda operator), 386–390 A accumulative functions, 150 accuracy of classification, 239 ACL (Association for Computational Linguistics), 34 Special Interest Group on Web as Corpus (SIGWAC), 416 adjectives, categorizing and tagging, 186 adjuncts of lexical head, 347 adverbs, categorizing and tagging, 186
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assert statements using in defensive programming, 159 using to find logical errors, 146 assignment, 130, 378 defined, 14 to list index values, 13 Association for Computational Linguistics (see ACL) associative arrays, 189 assumptions, 369 atomic values, 336 attribute value matrix, 336 attribute-value pairs (Toolbox lexicon), 67 attributes, XML, 426 auxiliaries, 348
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operators numerical, 22 for words, 23 complements of lexical head, 347 complements of verbs, 313 complex types, 373 complex values, 336 components, language understanding, 31 computational linguistics, challenges of natural language, 441 computer understanding of sentence meaning, 368 concatenation, 11, 88 lists and strings, 87 strings, 16 conclusions in logic, 369 concordances
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language technology research group and has taught at all levels of the undergraduate computer science curriculum. In 2009, Steven is President of the Association for Computational Linguistics. Ewan Klein is Professor of Language Technology in the School of Informatics at the University of Edinburgh. He completed a Ph.D. on formal semantics
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of Edify Corporation, Santa Clara, and was responsible for spoken dialogue processing. Ewan is a past President of the European Chapter of the Association for Computational Linguistics and was a founding member and Coordinator of the European Network of Excellence in Human Language Technologies (ELSNET). Edward Loper has recently completed a Ph
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.D. on machine learning for natural language processing at the University of Pennsylvania. Edward was a student in Steven’s graduate course on computational linguistics in the fall of 2000, and went on to be a Teacher’s Assistant and share in the development of NLTK. In addition to NLTK
The Language Instinct: How the Mind Creates Language
by Steven Pinker · 1 Jan 1994 · 661pp · 187,613 words
a new one, like toothbrush and mouse-eater. Thanks to these processes, the number of possible words, even in morphologically impoverished English, is immense. The computational linguist Richard Sproat compiled all the distinct words used in the forty-four million words of text from Associated Press news stories beginning in mid-February
Darwin's Dangerous Idea: Evolution and the Meanings of Life
by Daniel C. Dennett · 15 Jan 1995 · 846pp · 232,630 words
was linguistics before Chomsky. The contemporary scientific field of linguistics, with its subdisciplines of phonology, syntax, semantics, and pragmatics, its warring schools and renegade offshoots (computational linguistics in AI, for instance), its subdisciplines of psycholinguistics and neurolinguistics, grows out of various scholarly traditions going back to pioneer language sleuths and theorists from
Because Internet: Understanding the New Rules of Language
by Gretchen McCulloch · 22 Jul 2019 · 413pp · 106,479 words
Diakopoulos. 2011. “Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs.” Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. pp. 562–570. expressive lengthening: Tyler Schnoebelen. January 8, 2013. “Aww, hmmm, ohh heyyy nooo omggg!” Corpus Linguistics. corplinguistics.wordpress.com/2013/01/08/aww
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, Jure Leskovec, and Christopher Potts. 2013. “A Computational Approach to Politeness with Application to Social Factors.” Presented at 51st Annual Meeting of the Association for Computational Linguistics. arxiv.org/abs/1306.6078. study by Carol Waseleski: Carol Waseleski. 2006. “Gender and the Use of Exclamation Points in Computer-Mediated Communication: An Analysis
Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell · 14 Oct 2019 · 350pp · 98,077 words
. 16. K. Papineni et al., “BLEU: A Method for Automatic Evaluation of Machine Translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (2002), 311–18. 17. Wu et al., “Google’s Neural Machine Translation System”; H. Hassan et al., “Achieving Human Parity on Automatic Chinese to English
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., “Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers (2018), 2587–97. 28. N. Carlini and D. Wagner, “Audio Adversarial Examples: Targeted Attacks on Speech-to-Text,” in Proceedings of
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for Evaluating Reading Comprehension Systems,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017). 30. C. D. Manning, “Last Words: Computational Linguistics and Deep Learning,” Nautilus, April 2017. 14: On Understanding 1. G.-C. Rota, “In Memoriam of Stan Ulam: The Barrier of Meaning,” Physica D Nonlinear
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Case Study on Neural Image Captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers (2018), 2587–97. Reprinted with permission of Hongge Chen and the Association for Computational Linguistics. here Figure 44: Dorothy Alexander / Alamy Stock Photo. here Figure 45: From www.foundalis.com/res
These Strange New Minds: How AI Learned to Talk and What It Means
by Christopher Summerfield · 11 Mar 2025 · 412pp · 122,298 words
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Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
principle be programmed. Modern linguistics and AI, then, were “born” at about the same time, and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing. The problem of understanding language turned out to be considerably more complex than it seemed in 1957. Understanding language requires an
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treebank from which its probabilities were learned. There have been many attempts to write formal grammars of natural languages, both in “pure” linguistics and in computational linguistics. There are several comprehensive but informal grammars of English (Quirk et al., 1985; McCawley, 1988; Huddleston and Pullum, 2002). Since the 1980s, there has been
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in Natural Language Processing (EMNLP), and the journal Natural Language Engineering. A broad range of NLP work appears in the journal Computational Linguistics and its conference, ACL, and in the International Computational Linguistics (COLING) conference. Jurafsky and Martin (2020) give a comprehensive introduction to speech and NLP. 1And even computer vision applications: WordNet provides
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, 278 component (of mixture distribution), 790 composite decision process, 126 composite object, 336 compositionality, 269 compositional semantics, 894 computability, 27 computational learning theory, 690, 691 computational linguistics, 34, 904 computation graph, 805 computed torque control, 961 computer engineering, 32–33 computer vision, 30, 38, 186, 188, 989–1026 concession, 634 conclusion (of
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