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Part of the book series: Text, Speech and Language Technology ((TLTB,volume 33))

Anyone who gets the joke when they hear a pun will realize that lexical ambiguity is a fundamental characteristic of language: Words can have more than one distinct meaning. So why is it that text doesn’t seem like one long string of puns? After all, lexical ambiguity is pervasive. The 121 most frequent English nouns, which account for about one in five word occurrences in real text, have on average 7.8 meanings each (in the Princeton WordNet (Miller 1990), tabulated by Ng and Lee (1996)). But the potential for ambiguous readings tends to go completely unnoticed in normal text and flowing conversation. The effect is so strong that some people will even miss a pun (a real ambiguity) obvious to others. Words may be polysemous in principle, but in actual text there is very little real ambiguity – to a person.

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References

  • Allen, James. 1995. Natural Language Understanding. Redwood City, California: Benjamin Cummings.

    Google Scholar 

  • ALPAC. 1966. Language and Machine: Computers in Translation and Linguistics. A report by the Automatic Language Processing Advisory Committee, Division of Behavioral Sciences, National Research Council. Washington, D.C.: National Academy of Sciences.

    Google Scholar 

  • Atkins, Sue. 1991. Tools for computer-aided corpus lexicography: The Hector project. Acta Linguistica Hungarica, 41: 5-72.

    Google Scholar 

  • Baker, Collin F., Charles J. Fillmore & Beau Cronin. 2003. The structure of the FrameNet database. International Journal of Lexicography, 16(3): 281-296.

    Article  Google Scholar 

  • Bar-Hillel, Yehoshua. 1960. The present status of automatic translation of languages. Advances in Computers, ed. by Franz Alt et al. 91-163. New York: Academic Press.

    Google Scholar 

  • Bhattacharya, Indrajit, Lise Getoor & Yoshua Bengio. 2004. Unsupervised word sense disambiguation using bilingual probabilistic models. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, Spain, 288-295.

    Google Scholar 

  • Black, Ezra. 1988. An experiment in computational discrimination of English word senses. IBM Journal of Research and Development, 32(2): 185-194.

    Article  Google Scholar 

  • Bloehdorn, Stephan & Andreas Hotho. 2004. Text classification by boosting weak learners based on terms and concepts. Proceedings of the Fourth IEEE International Conference on Data Mining, 331-334.

    Google Scholar 

  • Brown, Peter F., Stephen Della Pietra, Vincent J. Della Pietra & Robert L. Mercer. 1991. Word-sense disambiguation using statistical methods. Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics (ACL), Berkeley, California, 264-270.

    Google Scholar 

  • Carpuat, Marine & Dekai Wu. 2005. Word sense disambiguation vs. statistical machine translation. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, Michigan, 387-394.

    Google Scholar 

  • Chapman, Robert. 1977. Roget’s International Thesaurus (Fourth Edition). New York: Harper and Row.

    Google Scholar 

  • Chlovski, Timothy & Rada Mihalcea. 2002. Building a sense tagged corpus with Open Mind Word Expert. Proceedings of the Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, Philadelphia, PA, USA, 116-122.

    Chapter  Google Scholar 

  • Clough, Paul & Mark Stevenson. 2004. Cross-language information retrieval using EuroWordNet and word sense disambiguation. Advances in Information Retrieval, 26th European Conference on IR Research (ECIR), Sunderland, UK, 327-337.

    Google Scholar 

  • Cowie, Jim, Joe A. Guthrie & Louise Guthrie. 1992. Lexical disambiguation using simulated annealing. Proceedings of the 14th International Conference on Computational Linguistics (COLING), Nantes, France, 359-365.

    Chapter  Google Scholar 

  • Cruse, D. Alan. 1986. Lexical Semantics. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Dagan, Ido, Oren Glickman & Bernardo Magnini. 2005. The PASCAL recognising textual entailment challenge. Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment.

    Google Scholar 

  • Dale, Robert, Hermann Moisl & Harold Somers, eds. 2000. Handbook of Natural Language Processing. New York: Marcel Dekker.

    Google Scholar 

  • Diab, Mona. 2003. Word Sense Disambiguation within a Multilingual Framework. Ph.D. Thesis, Department of Linguistics, University of Maryland, College Park, Maryland.

    Google Scholar 

  • Dill, Stephen, Nadav Eiron, David Gibson, Daniel Gruhl, R. Guha, Anant Jhingran, Tapas Kanungo, Sridhar Rajagopalan, Andrew Tomkins, John A. Tomlin & Jason Y. Zien. 2003. SemTag and Seeker: Bootstrapping the Semantic Web via automated semantic annotation. Proceedings of the Twelfth International Conference on World Wide Web (WWW-2003), Budapest, Hungary, 178-186.

    Chapter  Google Scholar 

  • Edmonds, Philip & Scott Cotton. 2001. Senseval-2: Overview. Proceedings of Senseval-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France, 1-5.

    Google Scholar 

  • Edmonds, Philip & Adam Kilgarriff. 2002. Introduction to the special issue on evaluating word sense disambiguation systems. Journal of Natural Language Engineering, 8(4): 279-291.

    Article  Google Scholar 

  • Edmonds, Philip. 2005. Lexical disambiguation. The Elsevier Encyclopedia of Language and Linguistics, 2nd Ed., ed. by Keith Brown, 607-23. Oxford: Elsevier.

    Google Scholar 

  • Fellbaum, Christiane, ed. 1998. WordNet: An Electronic Lexical Database. MIT Press.

    Google Scholar 

  • Gale, William, Kenneth Church & David Yarowsky. 1992. Estimating upper and lower bounds on the performance of word-sense disambiguation programs. Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics (ACL), Newark, Delaware, 249-256.

    Chapter  Google Scholar 

  • Gildea, Daniel & Daniel Jurafsky. 2002. Automatic labeling of semantic roles. Computational Linguistics, 28(3): 245-288.

    Article  Google Scholar 

  • Guthrie, Joe A., Louise Guthrie, Yorick Wilks & Homa Aidinejad. 1991. Subject dependent co-occurrence and word sense disambiguation. Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics (ACL), Berkeley, California, 146-152.

    Chapter  Google Scholar 

  • Hanks, Patrick. 2000. Do word meanings exist? Computers in the Humanities, 34(1-2): 205-215.

    Article  Google Scholar 

  • Hirst, Graeme. 1987. Semantic Interpretation and the Resolution of Ambiguity. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Ide, Nancy & Jean Véronis. 1998. Word sense disambiguation: The state of the art. Computational Linguistics, 24(1): 1-40.

    Google Scholar 

  • Jurafsky, Daniel & James H. Martin. 2000. Speech and Language Processing. New Jersey, USA: Prentice Hall.

    Google Scholar 

  • Kaplan, Abraham. 1950. An experimental study of ambiguity and context. Mimeographed, 18pp, November 1950. Published as: Kaplan, Abraham. 1955. An experimental study of ambiguity and context. Mechanical Translation, 2(2): 39-46.

    Google Scholar 

  • Kelly, Edward F. & Philip J. Stone. 1975. Computer Recognition of English Word Senses. Amsterdam: North-Holland.

    Google Scholar 

  • Kilgarriff, Adam. 1997. “I don’t believe in word senses”. Computers in the Humanities, 31(2): 91-113.

    Article  Google Scholar 

  • Kilgarriff, Adam & Martha Palmer. 2000. Introduction to the special issue on Senseval. Computers and the Humanities, 34(1-2): 1-13.

    Article  Google Scholar 

  • Karin Kipper, Hoa Trang Dang & Martha Palmer. 2000. Class-based construction of a verb lexicon. Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-2000), Austin, Texas.

    Google Scholar 

  • Lesk, Michael. 1986. Automated sense disambiguation using machine-readable dictionaries: How to tell a pine cone from an ice cream cone. Proceedings of the 1986 ACM SIGDOC Conference, Toronto, Canada, 24-26.

    Google Scholar 

  • Li, Hang & Cong Li. 2004. Word translation disambiguation using bilingual bootstrapping. Computational Linguistics, 30(1): 1-22.

    Article  Google Scholar 

  • Lyons, John. 1995. Linguistic Semantics: An Introduction. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Madhu, Swaminathan & Dean W. Lytle. 1965. A figure of merit technique for the resolution of non-grammatical ambiguity. Mechanical translation, 8(2): 9-13.

    Google Scholar 

  • Maedche, Alexander & Steffen Staab. 2001. Ontology learning for the Semantic Web. IEEE Intelligent Systems, 16(2): 72-79.

    Article  Google Scholar 

  • Manning, Christopher D. & Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Masterman, Margaret. 1957. The thesaurus in syntax and semantics. Mechanical Translation, 4(1-2): 35-43.

    Google Scholar 

  • McCarthy, Diana, Rob Koeling, Julie Weeds & John Carroll. 2004. Finding pre- dominant senses in untagged text. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL). Barcelona, Spain, 280 -287.

    Google Scholar 

  • Mihalcea, Rada, Timothy Chlovski & Adam Kilgarriff. 2004. The Senseval-3 English lexical sample task. Proceedings of Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, 25-28.

    Google Scholar 

  • Mihalcea, Rada & Philip Edmonds, eds. 2004. Proceedings of Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain.

    Google Scholar 

  • Miller, George A., ed. 1990. Special Issue, WordNet: An on-line lexical database. International Journal of Lexicography, 3(4).

    Google Scholar 

  • Ng, Hwee Tou & Hian Beng Lee. 1996. Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach. Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, Santa Cruz, California, 40-47.

    Chapter  Google Scholar 

  • Ng, Hwee Tou, Bin Wang & Yee Seng Chan. 2003. Exploiting parallel texts for word sense disambiguation: An empirical study. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL), Sapporo, Japan, 455-462.

    Chapter  Google Scholar 

  • Palmer, Martha, Christiane Fellbaum, Scott Cotton, Lauren Delfs & Hoa Trang Dang. 2001. English tasks: All-words and verb lexical sample. Proceedings of Senseval-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France, 21-24.

    Google Scholar 

  • Palmer, Martha, Christiane Fellbaum & Hoa Trang Dang. 2006. Making finegrained and coarse-grained sense distinctions, both manually and automatically. Natural Language Engineering, 12(3).

    Google Scholar 

  • Preiss, Judita & Mark Stevenson, eds. 2004. Computer, Speech, and Language, 18 (4). (Special issue on word sense disambiguation)

    Google Scholar 

  • Procter, Paul, ed. 1978. Longman Dictionary of Contemporary English. London: Longman Group.

    Google Scholar 

  • Quillian, M. Ross. 1968. Semantic memory. Semantic Information Processing, ed. by Marvin Minsky, 227-270. Cambridge, MA: MIT Press.

    Google Scholar 

  • Ravin, Yael & Claudia Leacock. 2000. Polysemy: Theoretical and Computational Approaches. Oxford University Press.

    Google Scholar 

  • Reifler, Edwin. 1955. The mechanical determination of meaning. Machine Translation of Languages, ed. William, Locke & Donald A. Booth, 136-164. New York: John Wiley & Sons.

    Google Scholar 

  • Resnik, Philip & David Yarowsky. 1999. Distinguishing systems and distinguishing senses: New evaluation methods for word sense disambiguation. Natural Language Engineering, 5(2): 113-133.

    Article  Google Scholar 

  • Rieger, Chuck & Steven Small. 1979. Word expert parsing. Proceedings of the 6th International Joint Conference on Artificial Intelligence (IJCAI), 723-728.

    Google Scholar 

  • Ruhl, Charles. 1989. On Monosemy: A Study in Linguistic Semantics. Albany: State University of New York Press.

    Google Scholar 

  • Sanderson, Mark. 1994. Word sense disambiguation and information retrieval. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, 142-151.

    Google Scholar 

  • Schütze, Hinrich. 1998. Automatic word sense discrimination. Computational Linguistics, 24(1): 97-123.

    Google Scholar 

  • Small, Steven, Garrison Cottrell & Michael Tanenhaus, eds. 1988. Lexical Ambiguity Resolution: Perspectives from Artificial Intelligence, Psychology and Neurolinguistics. San Mateo: Morgan Kaufman.

    Google Scholar 

  • Stevenson, Mark & Yorick Wilks. 2001. The interaction of knowledge sources in word sense disambiguation. Computational Linguistics, 27(3): 321-349.

    Article  Google Scholar 

  • Stevenson, Mark. 2003. Word Sense Disambiguation: The Case for Combination of Knowledge Sources. Stanford, USA: CSLI Publications.

    Google Scholar 

  • Tufiú, Dan, Radu Ion & Nancy Ide. Fine-grained word sense disambiguation based on parallel corpora, word alignment, word clustering, and aligned wordnets. Proceedings of the Twentieth International Conference on Computational Linguistics (COLING), Geneva, 1312-1318.

    Google Scholar 

  • Tuggy, David H. 1993. Ambiguity, polysemy, and vagueness. Cognitive Linguistics, 4: 273-90.

    Article  Google Scholar 

  • Véronis, Jean. 2004. Hyperlex: Lexical cartography for information retrieval. Computer, Speech and Language, 18(3): 223-252.

    Article  Google Scholar 

  • Voorhees, Ellen M. 1993. Using WordNet to disambiguate word senses for text retrieval. Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pittsburgh, Pennsylvania, 171-180.

    Chapter  Google Scholar 

  • Vossen, Piek, German Rigau, Iñaki Alegria, Eneko Agirre, David Farwell & Manuel Fuentes. 2006. Meaningful results for information retrieval in the MEANING project. Proceedings of the 3rd Global Wordnet Conference, Jeju Island, Korea.

    Google Scholar 

  • Weaver, Warren. 1949. Translation. Mimeographed, 12 pp. Reprinted in William N. Locke & Donald A. Booth, eds. 1955. Machine Translation of Languages, 15-23. New York: John Wiley & Sons.

    Google Scholar 

  • Weiss, Stephen. 1973. Learning to disambiguate. Information Storage and Retrieval, 9: 33-41.

    Article  Google Scholar 

  • Wilks, Yorick. 1975. Preference semantics. Formal Semantics of Natural Language, ed. by E. L. Keenan, III, 329-348. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Wilks, Yorick, Dan Fass, Cheng-Ming Guo, James E. MacDonald, Tony Plate & Brian A. Slator. 1990. Providing machine tractable dictionary tools. Semantics and the Lexicon, ed. by James Pustejovsky, 341-401. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Wilks, Yorick, Louise Guthrie & Brian Slator. 1996. Electric Words. Cambridge, MA: MIT Press.

    Google Scholar 

  • Yarowsky, David. 1992. Word sense disambiguation using statistical models of Roget’s categories trained on large corpora. Proceedings of the 14 th International Conference on Computational Linguistics (COLING), Nantes, France, 454-460.

    Chapter  Google Scholar 

  • Yarowsky, David. 1995. Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL), Cambridge, MA, 189-196.

    Chapter  Google Scholar 

  • Yarowsky, David. 2000. Word-sense disambiguation. Handbook of Natural Language Processing, ed. by Dale et al. 629-654. New York: Marcel Dekker.

    Google Scholar 

  • Zipf, George Kingsley. 1949. Human Behaviour and the Principle of Least Effort: An introduction to human ecology. Cambridge, MA: Addison-Wesley. Reprinted by New York: Hafner, 1972.

    Google Scholar 

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Agirre, E., Edmonds, P. (2007). Introduction. In: Agirre, E., Edmonds, P. (eds) Word Sense Disambiguation. Text, Speech and Language Technology, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4809-8_1

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