The Future of Text-Meaning in Computational Linguistics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5246)


Writer-based and reader-based views of text-meaning are reflected by the respective questions “What is the author trying to tell me?” and “What does this text mean to me personally?” Contemporary computational linguistics, however, generally takes neither view. But this is not adequate for the development of sophisticated applications such as intelligence gathering and question answering. I discuss different views of text-meaning from the perspective of the needs of computational text analysis and the collaborative repair of misunderstanding.


Natural Language Processing Machine Translation Question Answering Computational Linguistics Interactive Dialogue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Allan, J.: HARD track overview in TREC 2005. In: The 14th Text REtrieval Conference (TREC 2005) Proceedings, NIST (2006)Google Scholar
  2. 2.
    Barwise, J., Perry, J.: Situations and Attitudes. MIT Press, Cambridge (1983)Google Scholar
  3. 3.
    Carberry, S.: Plan Recognition in Natural Language Dialogue. MIT Press, Cambridge (1990)Google Scholar
  4. 4.
    Corriveau, J.-P.: Time-constrained Memory: A reader-based approach to text comprehension. Lawrence Erlbaum Associates, Mahwah (1995)Google Scholar
  5. 5.
    Farwell, D., et al.: Interlingual annotation of multilingual text corpora and FrameNet. In: Boas, H. (ed.) Multilingual FrameNets in Computational Lexicography, Mouton de Gruyter (to appear)Google Scholar
  6. 6.
    Fish, S.: Is there a text in this class? The authority of interpretive communities. Harvard University Press (1980)Google Scholar
  7. 7.
    Forbus, K.D., et al.: Integrating natural language, knowledge representation and reasoning, and analogical processing to learn by reading. In: Proceedings, 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), Vancouver, pp. 1542–1547 (2007)Google Scholar
  8. 8.
    Grice, H.P.: Utterer’s meaning, sentence-meaning, and word-meaning. Foundations of Language 4, 225–242 (1968)Google Scholar
  9. 9.
    Grosz, B.J., Sidner, C.L.: Attention, intentions, and the structure of discourse. Computational Linguistics 12(3), 175–204 (1986)Google Scholar
  10. 10.
    Hirst, G.: Negotiation, compromise, and collaboration in interpersonal and human-computer conversations. In: Proceedings, Workshop on Meaning Negotiation, 18th National Conference on Artificial Intelligence (AAAI-2002), Edmonton, pp. 1–4 (2002)Google Scholar
  11. 11.
    Hirst, G.: Views of text-meaning in computational linguistics: Past, present, and future. In: Dodig-Crnkovic, G., Stuart, S. (eds.) Computation, Information, Cognition – The Nexus and the Liminal, pp. 270–279. Cambridge Scholars Publishing (2007)Google Scholar
  12. 12.
    Hollingsworth, B., Teufel, S.: Human annotation of lexical chains: Coverage and agreement measures. In: Workshop on Methodologies and Evaluation of Lexical Cohesion Techniques in Real-world Applications, Salvador, Brazil (2005)Google Scholar
  13. 13.
    Hovy, E.: Learning by reading: An experiment in text analysis. In: Sojka, P., Kopeček, I., Pala, K. (eds.) Text, Speech and Dialogue. LNCS (LNAI), vol. 4188, pp. 3–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Klebanov, B.B.: Using readers to identify lexical cohesive structures in texts. In: Proceedings, Student Research Workshop, 43\(^{\ rd}\) Annual Meeting of the Association for Computational Linguistics, Ann Arbor, pp. 55–60 (2005)Google Scholar
  15. 15.
    McRoy, S.: Abductive interpretation and reinterpretation of natural language utterances. Ph.D. thesis, Department of Computer Science, University of Toronto (1993)Google Scholar
  16. 16.
    McRoy, S., Hirst, G.: The repair of speech act misunderstandings by abductive inference. Computational Linguistics 21(4), 435–478 (1995)Google Scholar
  17. 17.
    Malrieu, J.P.: Evaluative Semantics. Routledge (1999)Google Scholar
  18. 18.
    Morris, J.: Readers perceptions of lexical cohesion in text. In: Proceedings of the 32nd annual conference of the Canadian Association for Information Science, Winnipeg (2004)Google Scholar
  19. 19.
    Morris, J., Hirst, G.: The subjectivity of lexical cohesion in text. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds.) Computing attitude and affect in text, pp. 41–48. Springer, Heidelberg (2005)Google Scholar
  20. 20.
    Nirenburg, S., Raskin, V.: Ontological Semantics. MIT Press, Cambridge (2004)Google Scholar
  21. 21.
    Olson, D.R.: From utterance to text: The bias of language in speech and writing. Harvard Educational Review 47(3), 257–281 (1977)MathSciNetGoogle Scholar
  22. 22.
    Olson, D.R.: The World on Paper. Cambridge University Press, Cambridge (1994)Google Scholar
  23. 23.
    Reddy, M.J.: The conduit metaphor: A case of frame conflict in our language about language. In: Ortony, A. (ed.) Metaphor and Thought, pp. 284–324. Oxford University Press, Oxford (1979)Google Scholar
  24. 24.
    Schank, R.C. (ed.): Conceptual Information Processing. North-Holland, Amsterdam (1975)zbMATHGoogle Scholar
  25. 25.
    Schank, R.C., Abelson, R.P.: Scripts, Plans, Goals and Understanding. Lawrence Erlbaum Associates, Mahwah (1977)zbMATHGoogle Scholar
  26. 26.
    Stoyanov, V., Cardie, C., Wiebe, J.: Multi-perspective question answering using the OpQA corpus. In: Proceedings, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, pp. 923–930 (2005)Google Scholar
  27. 27.
    Terasaki, A.: Pre-announcement sequences in conversation. Social Science Working Paper 99. University of California, Irvine (1976)Google Scholar
  28. 28.
    Ureel II, L., et al.: Question generation for learning by reading. In: Proceedings of the AAAI Workshop on Inference for Textual Question Answering, Pittsburgh, pp. 22–26 (2005)Google Scholar
  29. 29.
    Yoshida, S., et al.: Constructing and examining personalized cooccurrence-based thesauri on Web pages. In: Proceedings, 12th International World Wide Web Conference, Budapest (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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