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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Fitzpatrick, 1990] J. Bachenko and E. Fitzpatrick. A computational grammar of discourse-neutral prosodic phrasing in English. Computational Linguistics, 16:155–170, 1990. [Baldwin & Kim, 2010] Timothy Baldwin and Su Nam Kim. Multiword Expressions. In Nitin Indurkhya and Fred J. Damerau, editors, Handbook of
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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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, 1982] Kenneth Church and Ramesh Patil. Coping with syntactic ambiguity or how to put the block in the box on the table. American Journal of Computational Linguistics, 8:139–149, 1982. [Cohen and Hunter, 2004] K. Bretonnel Cohen and Lawrence Hunter. Natural language processing and systems biology. In Werner Dubitzky and Francisco
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:94–102, 1970. [Emele and Zajac, 1990] Martin C. Emele and Rémi Zajac. Typed unification grammars. In Proceedings of the 13th Conference on Computational Linguistics, pages 293– 298. Association for Computational Linguistics, Morristown, NJ, 1990. [Farghaly, 2003] Ali Farghaly, editor. Handbook for Language Engineers. CSLI Publications, Stanford, CA, 2003. [Feldman and Sanger, 2007] Ronen
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, and Prediction. Springer, second edition, 2009. [Hearst, 1992] Marti Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th Conference on Computational Linguistics (COLING), pages 539–545, 1992. [Heim and Kratzer, 1998] Irene Heim and Angelika Kratzer. Semantics in Generative Grammar. Blackwell, 1998. [Hirschman et al., 2005] Lynette
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T. Kasper and William C. Rounds. A logical semantics for feature structures. In Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics, pages 257–266. Association for Computational Linguistics, 1986. [Kathol, 1999] Andreas Kathol. Agreement and the syntax-morphology interface in HPSG. In Robert D. Levine and Georgia M. Green, editors
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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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and Pereira, 1982] David H. D. Warren and Fernando C. N. Pereira. An efficient easily adaptable system for interpreting natural language queries. American Journal of Computational Linguistics, 8(3-4):110–122, 1982. [Wechsler and Zlatic, 2003] Stephen Mark Wechsler and Larisa Zlatic. The Many Faces of Agreement. Stanford Monographs in Linguistics
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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 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
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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Artificial Intelligence AAMAS Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems ACL Proceedings of the Annual Meeting of the Association for Computational Linguistics AIJ Artificial Intelligence (Journal) AIMag AI Magazine AIPS Proceedings of the International Conference on AI Planning Systems AISTATS Proceedings of the International Conference on Artificial
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Communications of the Association for Computing Machinery COGSCI Proceedings of the Annual Conference of the Cognitive Science Society COLING Proceedings of the International Conference on Computational Linguistics COLT Proceedings of the Annual ACM Workshop on Computational Learning Theory CP Proceedings of the International Conference on Principles and Practice of Constraint Programming CVPR
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. Brown, P. F., Desouza, P. V., Mercer, R. L., Pietra, V. J. D., and Lai, J. C. (1992). Class-based n-grammodels of natural language. Computational linguistics, 18(4). Browne, C., Powley, E. J., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Liebana, D. P., Samothrakis, S., and
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Natural Language Processing. Church, K. and Patil, R. (1982). Coping with syntactic ambiguity or how to put the block in the box on the table. Computational Linguistics, 8, 139–149. Church, K. (2004). Speech and language processing: Can we use the past to predict the future. In Proc. Conference on Text, Speech
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description logic. In Proc. IJCAI-03 Configuration Workshop. Jurafsky, D. and Martin, J. H. (2020). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (3rd edition). Prentice-Hall. Kadane, J. B. and Simon, H. A. (1977). Optimal strategies for a class of constrained sequential problems. Annals
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the Human Mind. Mariner Books. Marcus, M. P., Santorini, B., and Marcinkiewicz, M. A. (1993). Building a large annotated corpus of English: The Penn treebank. Computational Linguistics, 19, 313–330. Marinescu, R. and Dechter, R. (2009). AND/OR branch-and-bound search for combinatorial optimization in graphical models. AIJ, 173, 1457–1491
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–262. Och, F. J. and Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29, 19–51. Och, F. J. and Ney, H. (2004). The alignment template approach to statistical machine translation. Computational Linguistics, 30, 417–449. Och, F. J. and Ney, H. (2002). Discriminative training and maximum entropy models
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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
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
The Stuff of Thought: Language as a Window Into Human Nature
by Steven Pinker · 10 Sep 2007 · 698pp · 198,203 words
meaning that is modified by good, sparing it from having to saddle the word good with dozens of meanings. What is this meaning component? The computational linguist James Pustejovsky argues that Aristotle got it right when he proposed that the mind understands every entity in terms of four causes: who or what
These Strange New Minds: How AI Learned to Talk and What It Means
by Christopher Summerfield · 11 Mar 2025 · 412pp · 122,298 words
, A. (2020), ‘Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data’, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5185–98. Available at https://doi.org/10.18653/v1/2020.acl-main.463. Bengio, Yoshua, Ducharme, Réjean, Vincent, Pascal, and Jauvin, Christian (2003
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/10.1126/science.165.3894.664. Gehman, S. et al. (2020), ‘RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models’, in Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 3356–69. Available at https://doi.org/10.18653/v1/2020.findings-emnlp.301. Glaese, A. et al. (2022), ‘Improving Alignment of
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October 2023). Linzen, T., Dupoux, E., and Goldberg, Y. (2016), ‘Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies’, Transactions of the Association for Computational Linguistics, 4, pp. 521–35. Available at https://doi.org/10.1162/tacl_a_00115. Liu, T. and Low, B. K. H. (2023), ‘Goat: Fine-Tuned
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, Liar, Pants on Fire”: A New Benchmark Dataset for Fake News Detection’, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 2: Short Papers, Vancouver: Association for Computational Linguistics, pp. 422–6. Available at https://doi.org/10.18653/v1/P17-2067. Webb, T. et al. (2023), ‘A Prefrontal Cortex
Geek Sublime: The Beauty of Code, the Code of Beauty
by Vikram Chandra · 7 Nov 2013 · 239pp · 64,812 words
teachers for other villages remain frustrated. The Special Centre for Sanskrit Studies at the Jawaharlal Nehru University in New Delhi has more explicit aims: Sanskrit computational linguistics, Sanskrit informatics, Sanskrit computing, Sanskrit language processing. There has also been an effort over the past two decades to reintroduce the Indian scholastic tradition into
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Early Winter by Jong89.” Dwarf Fortress Map Archive, 2009. http://mkv25.net/dfma/poi-22127-dwarvencomputer. Joshi, S. D. “Background of the Aṣṭādhyāyī.” In Sanskrit Computational Linguistics, 1–5. Springer, 2009. http://link.springer.com/chapter/10.1007/978-3-540-93885-9_1. Kapoor, Kapil. Dimensions of Pāṇini Grammar: The Indian
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://businesstoday.intoday.in/story/google-executive-chairman-eric-schmidt-on-india/1/193496.html. Kiparsky, Paul. “On the Architecture of Pāṇini’s Grammar.” In Sanskrit Computational Linguistics, 33–94. Springer, 2009. http://link.springer.com/chapter/10.1007/978-3-642-00155-0_2. ______. “Paninian Linguistics.” The Encyclopedia of Language and Linguistics
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from India.” 60 Minutes. CBS Video. June 22, 2003. Subbanna, Sridhar, and Srinivasa Varakhedi. “Computational Structure of the Aṣṭādhyāyī and Conflict Resolution Techniques.” In Sanskrit Computational Linguistics, 56–65. Springer, 2009. Swain, F. “Glowing Trees Could Light up City Streets.” New Scientist 208, no. 2788 (2010): 21. Swan, Rachel. “Outside the Gates
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
Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think
by James Vlahos · 1 Mar 2019 · 392pp · 108,745 words
. They specialized, focusing on problems such as automatic speech recognition, which is the process of converting the audio waveforms of speech into written words, and computational linguistics, which is the practice of statistically analyzing patterns in language use. (Only in the past decade have researchers began to unite the subdisciplines into full
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, Kenneth, 75 Cold War, 9, 71 Collins, Victor, 222–24 Colloquis, xiii Colossal Cave Adventure (video game), 78–79, 98, 253 common sense, 161–62 computational linguistics, 72 computational propaganda, 216–20 Computel, 107 Computer Power and Human Reason (Weizenbaum), 73 Concept Graph, 204–5, 212 concierge chatbots, 58 Connell, Derek, 130
AIQ: How People and Machines Are Smarter Together
by Nick Polson and James Scott · 14 May 2018 · 301pp · 85,126 words
. Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig, “Linguistic Regularities in Continuous Space Word Representations,” in Proceedings of NAACL-HLT, 2013 (Stroudsburg, PA: Association for Computational Linguistics, 2013), 746–51. CHAPTER 5 1. We distinctly remember hearing this piece of commentary on a TV show in the wake of the coin-flip
Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data
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Natural Language Annotation for Machine Learning
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The Science of Language
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The Language Instinct: How the Mind Creates Language
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