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Home > Archive>Volume 22, Issue 2, 2011 >233-244. DOI:10.3724/SP.J.1001.2011.03720
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Pronoun Resolution in Spoken Dialog
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    Abstract:

    This paper presents a two-stage pronoun resolution algorithm. It does not need to clean the testing corpus and predefine patterns manually. In the first stage of the algorithm, some new features and machine learning methods are used to classify pronouns into anaphoric and non-anaphoric ones. In the second stage, these two kinds of pronouns are resolved respectively. For the anaphoric ones, some methods are presented to extract distance, syntactic, and semantic features etc. For the non-anaphoric ones, the Right Frontier Rule is improved to do the resolution work. While testing the corpus published by Byron in 2004, this algorithm achieves a precision of 77.0% and a recall of 66.0%. Compared with the work of Byron, the algorithm is fully automatic, and the results are much better.

    Reference
    [1] Lemon O, Gruenstein A, Peters SCollaborative activities and multi-tasking in dialog system. Traitment Atomatique des Lngues, Special Issue on Dialogue, 2002,43(2):131?154.
    [2] Eckert M, Strube M. Dialogue acts, synchronizing units and anaphora resolution. Journal of Semantics, 2000,17(1):51?89. [doi: 10.1093/jos/17.1.51]
    [3] Strube M, Müller C. A machine learning approach to pronoun resolution in spoken dialog. In: Tsujii J, ed. Proc. of the Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2003. 168?175. [doi: 10.3115/1075096.1075118]
    [4] Byron DK. Resolving pronominal reference to abstract entities [Ph.D. Thesis]. Rochester: University of Rochester, 2004.
    [5] Tetreault J, Allen J. Semantics, dialogue, and reference resolution. In: Proc. of the CATALOG. 2004. http://www.cs.rochester.edu/u/tetreaul/sd04.pdf
    [6] Pappuswamy U, Jordan PW, Van Lehn K. Resolving discourse deictic anaphors in tutorial dialogues. In: Proc. of the Constraints in Discourse. 2005. 96?103. http://www.public.asu.edu/~kvanlehn/Stringent/PDF/05CSS_UP_PWJ_KVL.pdf
    [7] Eckert M, Strube M. Resolving discourse deictic anaphora in dialogues. In: Proc. of the 9th Conf. of the European Chapter of the Association for Computational Linguistics (EACL). Morristown: ACL, 1999. 37?44. http://acl.ldc.upenn.edu/E/E99/E99-1006.pdf [doi: 10.3115/ 977035.977042]
    [8] Bergsma S, Lin DK, Goebel R. Distributional identification of non-referential pronouns. In: McKeown K, ed. Proc. of the Annual Meeting on Association for Computational Linguistics (ACL 2008: HLT). Morristown: ACL, 2008. 10?18.
    [9] Evans R. Applying machine learning toward an automatic classification of it. Literary and Linguistic Computing, 2001,16(1): 45?57. [doi: 10.1093/llc/16.1.45]
    [10] Dimitrov M, Bontcheva K, Cunningham H, Maynard D. A light-weight approach to coreference resolution for named entities in text. In: Branco A, et al., eds. Proc. of the 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC). Lisboa: Edi??es Colibri, 2002. http://www.gate.ac.uk/sale/daarc2002/DAARC2002.pdf
    [11] Webber B. Structure and ostention in the interpretation of discourse deixis. Language and Cognitive Processes, 1991,6:107?135. [doi: 10.1080/01690969108406940]
    [12] Tetreault JR. Analysis of syntax-based pronoun resolution methods. In: Proc. of the 37th Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 1999. 602?605. [doi: 10.3115/1034678.1034688]
    [13] Müller C. Resolving it, this, and that in unrestricted multi-party dialog. In: Carroll J, ed. Proc. of the Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2007. 816?823.
    [14] Boyd A, Gegg-Harrison W, Byron D. Identifying non-referential it: A machine learning approach incorporating linguistically motivated patterns. In: Proc. of the ACL Workshop on Feature Selection for Machine Learning in NLP. 2005. 40?47. http://acl.ldc.upenn.edu/W/W05/W05-0406.pdf
    [15] Müller C. Automatic detection of nonreferential it in spoken multi-party dialog. In: McCarthy D, Wintner S, eds. Proc. of the 11th Conf. of the European Chapter of the Association for Computational Linguistics (EACL). Morristown: ACL, 2006. 49?56.
    [16] Ng V, Cardie C. Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution. In: Proc. of the 19th Int’l Conf. on Computational Linguistics (COLING). Morristown: ACL, 2002. 730?736. http://www.cs.cornell.edu/yung/papers/coling02.ps [doi: 10.3115/1072228.1072367]
    [17] Litrán JCC, Satou K, Torisawa K. Improving the identification of non-anaphoric “it” using support vector machines. In: Proc. of the Int’l Joint Workshop on Natural Language Processing in Biomedicine and its Applications. 2004. 58?61. http://acl.ldc.upenn.edu/W/W04/W04-1210.pdf
    [18] Ng V, Cardie C. Improving machine learning approaches to coreference resolution. In: Isabelle P, ed. Proc. of the 40th Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2002. 104?111. [doi: 10.3115/1073083.1073102]
    [19] Lappin S, Leass HJ. An algorithm for pronominal anaphora resolution. Computational Linguistics, 1994,20(4):535?561.
    [20] Grosz BJ, Joshi AK, Weinstein S. Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 1995,21(2):203?225.
    [21] Woei-A-Jin JLRD. Reference resolution in a speech recognition environment. Technical Report, DKS04-01, Delft: Delft University of Technology, 2001.
    [22] Wang HF. Survey: Computational models and technologies in anaphora resolution. Journal of Chinese Information Processing, 2002,16(6):9?17 (in Chinese with English abstract).
    [23] Wang ZQ. Research on Chinese coreference resolution and its related technologies [Ph.D. Thesis]. Beijing: Beijing Posts and Telecommunications University, 2006 (in Chinese with English abstract).
    [24] Ng V. Shallow semantics for coreference resolution. In: Proc. of the Joint Conf. on Artificial Intelligence (IJCAI). IJCAI, 2007. 1689?1694. http://www.hlt.utdallas.edu/~vince/talks/ijcai07.pdf
    [25] Yang XF, Su J, Tan CL. Improving pronoun resolution using statistics-based semantic compatibility information. In: Knight K, ed. Proc. of the 43rd Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2005. 33?40. [doi: 10.3115/ 1219840.1219861]
    [26] Yang XF, Su J. Coreference resolution using semantic relatedness information from automatically discovered patterns. In: Carroll J, ed. Proc. of the 45th Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2007. 528?535.
    [27] Navarretta C. Resolving individual and abstract anaphora in texts and dialogues. In: Proc. of the 20th Int’l Conf. of Computational Linguistics. Morristown: ACL, 2004. 233?239. http://acl.ldc.upenn.edu/C/C04/C04-1034.pdf [doi: 10.3115/1220355.1220389]
    [28] Chomsky N. Lectures on Government and Binding: The Pisa Lectures. Dordrecht: Foris Publications, 1981.
    [29] Reinhart T. Binding Theory. In: Wilson RA, Keil FC, eds. The MIT Encyclopedia of the Cognitive Sciences. Cambridge: MIT Press, 1999. 86?88.
    [30] Ge NY, Hale J, Charniak E. A statistical approach to anaphora resolution. In: Charniak E, ed. Proc. of the 6th Workshop on Very Large Corpora. Montréal: Université de Montréal, 1998. 161?170.
    [31] Soon WM, Ng HT, Lim DCY. A machine learning approach to conference resolution of noun phrases. Computational Linguistics, 2001,27(4):521?544. [doi: 10.1162/089120101753342653]
    [32] Qian W, Guo YK, Zhou YQ, Wu LD. English noun phrase coreference resolution via a maximum entropy model. Journal of Computer Research and Development, 2003,40(9):1337?1342 (in Chinese with English abstract).
    [33] Artstein R, Poesio M. Identifying reference to abstract objects in dialogue. In: Schlangen D, Fernández R, eds. Proc. of the 10th Workshop on the Semantics and Pragmatics of Dialogue. Potsdam: Universit?t Potsdam, 2006. 56?63.
    [34] Artstein R. Quality control of corpus annotation through reliability measures. In: Proc. of the Annual Meeting on Association for Computational Linguistics. Morristown: ACL, 2007. Http://ron.artstein.org/publications/2007-acl-t5-slides.pdf
    [35] Baldwin B. CogNIAC: High precision coreference with limited knowledge and linguistic resources. In: Proc. of the ACL-EACL Workshop on Operational Factors in Practical, Robust Anaphora Resolution. Morristown: ACL, 1997. 38?45.
    [36] Gaizauskas R, Humphreys K. Quantitative evaluation of coreference algorithms in an information extraction system. In: Proc. of the Discourse Anaphora and Anaphor Resolution Colloquium (DAARC-1). London: University College London Press, 1996.
    [37] Harabagiu SM. Maiorano SJ. Knowledge-Lean coreference resolution and its relation to textual cohesion and coherence. In: Proc. of the ACL Workshop on the Relation of Discourse/Dialogue Structure and Reference. Morristown: ACL, 1999. 29?38. http://acl.ldc.upenn.edu/W/W99/W99-0104.pdf
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费仲超,周雅倩,黄萱菁,吴立德.口语对话中的代词指代消解.软件学报,2011,22(2):233-244

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History
  • Received:February 20,2009
  • Revised:August 12,2009
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