Identification of pleonastic it using the Web

Journal of Artificial Intelligence Research

Volumn 34, Issue , 2009, Pages 339-389

  Li, Yifan ^a   Musilek, Petr ^a   Reformat, Marek ^a   Wyard Scott, Loren ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRONICS ENGINEERING;

NATURAL LANGUAGE TEXT; NEWS ARTICLES; SYNTACTIC RULES;

SYNTACTICS;

EID: 65349140141     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2622     Document Type: Article

References (50)

1. Agresti, A., &Coull, B. A, (1998). Approximate is better than "exact" for interval estimation of binomial proportions. The American Statistician, 52(2), 119-126.
2. Berland, M., &Charniak, E. (1999). Finding parts in very large corpora. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 57-64.
3. Bies, A., Ferguson, M., Katz, K., &Maclntyre, R. (1995). Bracketing guidelines for Tree- bank II style. Tech. rep. MS-CIS-95-06, Department of Computer and Information Science, University of Pennsylvania.
4. Boyd, A., Gegg-Harrison, W., &Byron, D. (2005). Identifying non-referential it: A machine learning approach incorporating linguistically motivated patterns. In Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing, pp. 40-47. Association for Computational Linguistics.
5. Charniak, E., &Johnson, M. (2005). Coarse-to-fine n-best parsing and maxent discriminative reranking. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (AGL05), pp. 173-180, Morristown, NJ, USA. Association for Computational Linguistics.
6. Chinchor, N. (1992). The statistical significance of the MUC-4 results. In Proceedings of the 4th conference on Message understanding (MUC'4), pp. 30-50, San Mateo, CA. Morgan Kaufmann.
7. Cimiano, P., Schmidt-Thieme, L., Pivk, A., &Staab, S. (2005). Learning taxonomic relations from heterogeneous evidence. In Buitelaar, P., Cimiano, P., &Magnini, B. (Eds.), Ontology Learning from Text: Methods. Applications and Evaluation, Frontiers in Artificial Intelligence and Applications, pp. 59-73. IOS Press, Amsterdam.
8. Clemente, J. C, Torisawa, K., &Satou, K. (2004). Improving the identification of non- anaphoric it using support vector machines. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA /BioNLP04 ).
9. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46.
10. Collins, M. (1999). Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania,
11. Davidse, K. (2000). A constructional approach to clefts. Linguistics, 38(6), 1101-1131.
12. Davison, A. C, &Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge series on statistical and probabilistic mathematics. Cambridge University Press, Cambridge, UK.
13. Denber, M. (1998). Automatic resolution of anaphora in English. Tech. rep., Eastman Kodak Co.
14. Di Eugenio, B., &Glass, M. (2004). The kappa statistic: a second look. Computational Linguistics, 30(1), 95-101.
15. Efron, B., &Tibshirani, R. (1993). An Introduction to the Bootstrap. Chapman and Hall, New York, USA.
16. Evans, R. (2000). A comparison of rule-based and machine learning methods for identifying non-nominal it. In Christodoulakis, D. (Ed.), Proceedings of the 2nd International Conference on Natural Language Processing (NLPOO), Vol. 1835 of Lecture Notes in Computer Science, pp. 233-241, Berlin. Springer.
17. Evans, R. (2001). Applying machine learning toward an automatic classification of it. Literary and Linguistic Computing, 16(1), 45-57.
18. Fellbaum, C. (Ed.). (1998). WordNet: An Electronic Lexical Database. The MIT Press. Cambridge, Mass., USA.
19. Geurts, B., &van der Sandt, R. (2004). Interpreting focus. Theoretical Linguistics, 30(1), 1-44.
20. Green, P. S., &Hecht, K. (1992). Implicit and explicit grammar: An empirical study. Applied Linguistics, 13(2), 168-184.
21. Gundel, J., Hedberg, N., &Zacharski, R. (2005). Pronouns without NP antecedents: How do we know when a pronoun is referential?. In Branco, A., McEnery, T., &Mitkov, R. (Eds.), Anaphora Processing: Linguistic, Cognitive and Computational Modelling, pp. 351-364. John Benjamins, Amsterdam, The Netherlands.
22. Gundel, J. K. (1977). Where do cleft sentences come from?. Language, 55(3), 543-559.
23. Hamawand, Z. (2003). For-to complement clauses in English: A cognitive grammar analysis. Studia Linguistica, 57(3), 171-192.
24. Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th international conference on Computational Linguistics, pp. 539-545.
25. Hedberg, N. (1990). The Discourse Function of Cleft Sentences in English. Ph.D. thesis, University of Minnesota.
26. Hedberg, N. (2000). The referential status of clefts. Language, 76(4), 891-920.
27. Kaltenböck, G. (2005). It-extraposition in English: A functional view. International Journal of Corpus Linguistics, 10(2), 119-159.
28. Kilgarriff, A. (2007). Googleology is bad science. Computational Linguistics, 33(1), 147-151.
29. Kilgarriff, A., &Grefenstette, G. (2003). Introduction to the special issue on the Web as corpus. Computational Linguistics, 29(3), 333-347.
30. Krifka, M. (2003). Bare NPs: Kind-referring, indefinites, both, or neither?. In Proceedings of Semantics and Linguistic Theory (SALT) XIII, New York, USA. CLC Publications.
31. Krippendorff, K. (1980). Content Analysis: An Introduction to Methodology. Sage Publications, Inc., Beverly Hills, USA.
32. Lambrecht, K. (2001). A framework for the analysis of cleft constructions. Linguistics, 39(3), 463-516.
33. Lappin, S., &Leass, H. J. (1994). An algorithm for pronominal anaphora resolution. Computational Linguistics, 20(4), 535-561.
34. Marcus, M. P., Marcinkiewicz, M. A., &Santorini, B. (1993). Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2), 313-330.
35. Markert, K., &Nissim, M. (2005). Comparing knowledge sources for nominal anaphora resolution. Computational Linguistics, 31(3), 367-402.
36. Markert, K., Nissim, M. &Modjeska, N. N. (2003). Using the web for nominal anaphora resolution. In Dale, R., van Deemter, K., &Mitkov, R. (Eds.), Proceedings of the EACL Workshop on the Computational Treatment of Anaphora, pp. 39-46.
37. Metcalf, A., &Barnhart, D. K. (1999). America in So Many Words: Words That Have Shaped America. Houghton Mifflin, Boston, USA.
38. Mitkov, R. (2001). Outstanding issues in anaphora resolution. In Gelbukh, A. (Ed.), Proceedings of the 2nd International Conference on Computational Linguistics and Intelligent Text Processing (CICLingOl), Vol. 2004 of Lecture Notes in Computer Science, pp. 110-125, Berlin. Springer.
39. Mitkov, R., Evans, R., &Orasan, C. (2002). A new, fully automatic version of Mitkov's knowledge-poor pronoun resolution method. In Gelbukh, A. F. (Ed.), Proceedings of the 3rd International Conference on Computational Linguistics and Intelligent Text Processing (CICLing02), Vol. 2276 of Lecture Notes in Computer Science, pp. 168-186, London, UK. Springer-Verlag.
40. Müller, C. (2006). Automatic detection of nonreferential it in spoken multi-party dialog. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL06), pp. 49-56.
41. Nanni, D. L. (1980). On the surface syntax of constructions with easy-type adjectives. Language, 56(3), 568-581.
42. Ng, V., &Cardie, C. (2002). Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution. In Proceedings of the 19th international conference on Computational linguistics (COLING02), pp. 1-7, Morristown, NJ, USA. Association for Computational Linguistics.
43. Noreen, E. W. (1989). Computer-Intensive Methods for Testing Hypotheses : An Introduction. Wiley-Interscience, New York, USA.
44. Paice, C. D., &Husk, G. D. (1987). Towards the automatic recognition of anaphoric features in english text: the impersonal pronoun "it". Computer Speech &Language, 2(2), 109-132.
45. Petrov, S., Barrett, L., Thibaux, R., &Klein, D. (2006). Learning accurate, compact, and interpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL (ACL06), pp. 433-440, Morristown, NJ, USA. Association for Computational Linguistics.
46. Poesio, M., Ishikawa, T., im Walde, S. S., &Vieira, R. (2002). Acquiring lexical knowledge for anaphora resolution. In Proceedings of the Third International Conference on Language Resources and Evaluation, pp. 1220-1224.
47. Sinclair, J. (Ed.). (1995). Collins COBUILD English Grammar. Harper Collins, London, U.K.
48. Sloat, C. (1969). Proper nouns in English. Language, 45(1), 26-30.
49. van Rijsbergen, C. J. (1979). Information Retrieval (2nd edition). Butterworth-Heinemann, Newton, MA, USA.
50. Xia, F., &Palmer, M. (2001). Converting dependency structures to phrase structures. In Proceedings of the first international conference on Human language technology research (HLT01), pp. 1-5, Morristown, NJ, USA. Association for Computational Linguistics.