Skip to main content

Discourse

  • Chapter
  • First Online:
  • 2835 Accesses

Part of the book series: Cognitive Technologies ((COGTECH))

Abstract

The grammatical concepts we have seen so far apply mostly to isolated words, phrases, or sentences. Texts and conversations, either full or partial, are out of their scope. Yet to us, human readers, writers, and speakers, language goes beyond the simple sentence. It is now time to describe models and processing techniques to deal with a succession of sentences. Although analyzing texts or conversations often requires syntactic and semantic treatments, it goes further. In this chapter, we shall make an excursion to the discourse side, that is, paragraphs, texts, and documents. In the next chapter, we shall consider dialogue, that is, a spoken or written interaction between a user and a machine.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832–843.

    Article  MATH  Google Scholar 

  • Allen, J. F. (1984). Towards a general theory of action and time. Artificial Intelligence, 23(2), 123–154.

    Article  MATH  Google Scholar 

  • Bagga, A., & Baldwin, B. (1998). Algorithms for scoring coreference chains. In Proceedings of the linguistic coreference workshop at the first international conference on language resources and evaluation, Granada (pp. 563–566).

    Google Scholar 

  • Björkelund, A., & Nugues, P. (2011). Exploring lexicalized features for coreference resolution. In Proceedings of the 15th conference on computational natural language learning (CoNLL-2011): Shared task, Portland (pp. 45–50).

    Google Scholar 

  • Bunescu, R., & Paşca, M. (2006). Using encyclopedic knowledge for named entity disambiguation. In Proceedings of the 11th conference of the European chapter of the association for computational linguistics, Trento (pp. 9–16). Association for Computational Linguistics.

    Google Scholar 

  • Carlson, L., Marcu, D., & Okurowski, M. (2003). Building a discourse-tagged corpus in the framework of rhetorical structure theory. In Current and new directions in discourse and dialogue (Text, speech and language technology, Vol. 22, pp. 85–112). Dordrecht: Springer.

    Google Scholar 

  • Corbett, E. P. J., & Connors, R. J. (1999). Classical rhetoric for the modern student (4th ed.). New York, Oxford University Press.

    Google Scholar 

  • Corston-Oliver, S. (1998). Computing representations of the structure of written discourse. PhD thesis, Linguistics Department, the University of California, Santa Barbara.

    Google Scholar 

  • Coulthard, M. (1985). An introduction to discourse analysis (2nd ed.). Harlow: Longman.

    Google Scholar 

  • Cucerzan, S. (2007). Large-scale named entity disambiguation based on wikipedia data. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning, Prague (pp. 708–716). Association for Computational Linguistics.

    Google Scholar 

  • Davidson, D. (1966). The logical form of action sentences. In N. Rescher (Ed.), The logic of decision and action. Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Ducrot, O., & Schaeffer, J.-M. (Eds.). (1995). Nouveau dictionnaire encyclopédique des sciences du langage. Paris: Éditions du Seuil.

    Google Scholar 

  • Finkel, J. R., Grenager, T., & Manning, C. (2005). Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43nd annual meeting of the association for computational linguistics (ACL 2005), Ann Arbor (pp. 363–370).

    Google Scholar 

  • Gagnon, M., & Lapalme, G. (1996). From conceptual time to linguistic time. Computational Linguistics, 22(1), 91–127.

    Google Scholar 

  • Gosselin, L. (1996). Sémantique de la temporalité en français: Un modèle calculatoire et cognitif du temps et de l’aspect. Louvain-la-Neuve: Duculot.

    Google Scholar 

  • Grosz, B. J., Joshi, A. K., & Weinstein, S. (1995). Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 21(2), 203–225.

    Google Scholar 

  • Grosz, B. J., & Sidner, C. L. (1986). Attention, intention, and the structure of discourse. Computational Linguistics, 12(3), 175–204.

    Google Scholar 

  • Hirschman, L., & Chinchor, N. (1997). MUC-7 coreference task definition. Technical report, Science Applications International Corporation.

    Google Scholar 

  • Hobbs, J. R., Appelt, D. E., Bear, J., Israel, D., Kameyama, M., Stickel, M., & Tyson, M. (1997). FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In E. Roche & Y. Schabes (Eds.), Finite-state language processing (chapter 13, pp. 383–406). Cambridge: MIT.

    Google Scholar 

  • Hoffart, J., Yosef, M. A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., & Weikum, G. (2011). Robust disambiguation of named entities in text. In Proceedings of the 2011 conference on empirical methods in natural language processing, Edinburgh (pp. 782–792).

    Google Scholar 

  • Huls, C., Claassen, W., & Bos, E. (1995). Automatic referent resolution of deictic and anaphoric expressions. Computational Linguistics, 21(1), 59–79.

    Google Scholar 

  • Ingria, B., & Pustejovsky, J. (2004). TimeML: A formal specification language for events and temporal expressions. Cited April 13, 2010, from http://www.timeml.org/site/publications/timeMLdocs/timeml_1.2.html

  • Johansson, R., Berglund, A., Danielsson, M., & Nugues, P. (2005). Automatic text-to-scene conversion in the traffic accident domain. In IJCAI-05, proceedings of the nineteenth international joint conference on artificial intelligence, Edinburgh (pp. 1073–1078).

    Google Scholar 

  • Kameyama, M. (1997). Recognizing referential links: An information extraction perspective. In R. Mitkov & B. Boguraev (Eds.), Proceedings of ACL workshop on operational factors in practical, robust anaphora resolution for unrestricted texts, Madrid (pp. 46–53).

    Google Scholar 

  • Kamp, H., & Reyle, U. (1993). From discourse to logic: Introduction to modeltheoretic semantics of natural language, formal logic and discourse representation theory. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Luo, X. (2005). On coreference resolution performance metrics. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, Vancouver (pp. 25–32).

    Google Scholar 

  • Mann, W. C., & Thompson, S. A. (1987). Rhetorical structure theory: A theory of text organization. Technical report RS-87-190, Information Sciences Institute of the University of Southern California.

    Google Scholar 

  • Mann, W. C., & Thompson, S. A. (1988). Rhetorical structure theory: Toward a functional theory of text organization. Text, 8, 243–281.

    Google Scholar 

  • Marcu, D. (1997). The rhetorical parsing, summarization, and generation of natural language texts. PhD thesis, Department of Computer Science, University of Toronto.

    Google Scholar 

  • Perelman, C., & Olbrechts-Tyteca, L. (1976). Traité de l’argumentation: la nouvelle rhétorique. Brussels: Éditions de l’Université de Bruxelles.

    Google Scholar 

  • Pradhan, S., Moschitti, A., Xue, N., Uryupina, O., & Zhang, Y. (2012). CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. In Proceedings of the joint conference on EMNLP and CoNLL: Shared task, Jeju Island (pp. 1–40). Association for Computational Linguistics.

    Google Scholar 

  • Pradhan, S., Ramshaw, L., Marcus, M., Palmer, M., Weischedel, R., & Xue, N. (2011). CoNLL-2011 shared task: Modeling unrestricted coreference in OntoNotes. In Proceedings of the fifteenth conference on computational natural language learning: Shared task, Portland (pp. 1–27). Association for Computational Linguistics.

    Google Scholar 

  • Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., & Webber, B. (2008). The Penn discourse treebank 2.0. In Proceedings of the 6th international conference on language resources and evaluation, Marrakech.

    Google Scholar 

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

    Google Scholar 

  • Raghunathan, K., Lee, H., Rangarajan, S., Chambers, N., Surdeanu, M., Jurafsky, D., & Manning, C. (2010). A multi-pass sieve for coreference resolution. In Proceedings of the 2010 conference on empirical methods in natural language processing, Cambridge, MA (pp. 492–501). Association for Computational Linguistics.

    Google Scholar 

  • Reboul, O. (1994). Introduction à la rhétorique: théorie et pratique (2nd ed.). Paris: Presses universitaires de France.

    Google Scholar 

  • Reichenbach, H. (1947). Elements of symbolic logic. New York: Macmillan.

    Google Scholar 

  • Schiffrin, D. (1994). Approaches to discourse (Number 8 in Blackwell textbooks in linguistics). Oxford: Blackwell.

    Google Scholar 

  • Simone, R. (2007). Fondamenti di linguistica (10th ed.). Bari: Laterza.

    Google Scholar 

  • Singhal, A. (2012). Introducing the knowledge graph: Things, not strings. Official Google Blog. Retrieved November 7, 2013, from http://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html

  • Soon, W. M., Ng, H. T., & Lim, D. C. Y. (2001). A machine learning approach to coreference resolution of noun phrases. Computational Linguistics, 27(4), 521–544.

    Article  Google Scholar 

  • Suri, L. Z., & McCoy, K. F. (1994). RAFT/RAPR and centering: A comparison and discussion of problems related to processing complex sentences. Computational Linguistics, 20(2), 301–317.

    Google Scholar 

  • Ter Meulen, A. (1995). Representing time in natural language. The dynamic interpretation of tense and aspect. Cambridge, MA: MIT.

    Google Scholar 

  • Tesnière, L. (1966). Éléments de syntaxe structurale (2nd ed.). Paris: Klincksieck.

    Google Scholar 

  • Tjong Kim Sang, E. F., & De Meulder, F. (2003). Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of CoNLL-2003, Edmonton (pp. 142–147).

    Google Scholar 

  • Vendler, Z. (1967). Linguistics in philosophy. Ithaca: Cornell University Press.

    Google Scholar 

  • Vilain, M., Burger, J., Aberdeen, J., Connolly, D., & Hirschman, L. (1995). A model-theoretic coreference scoring scheme. In Proceedings of the conference on sixth message understanding conference (MUC-6), Columbia (pp. 45–52).

    Google Scholar 

  • Zhang, T., & Johnson, D. (2003). A robust risk minimization based named entity recognition system. In Proceedings of CoNLL-2003, Edmonton (pp. 204–207).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Nugues, P.M. (2014). Discourse. In: Language Processing with Perl and Prolog. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41464-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41464-0_16

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41463-3

  • Online ISBN: 978-3-642-41464-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics