Abstract
This paper presents our recent work on period disambiguation, the kernel problem in sentence boundary identification, with the maximum entropy (Maxent) model. A number of experiments are conducted on PTB-II WSJ corpus for the investigation of how context window, feature space and lexical information such as abbreviated and sentence-initial words affect the learning performance. Such lexical information can be automatically acquired from a training corpus by a learner. Our experimental results show that extending the feature space to integrate these two kinds of lexical information can eliminate 93.52% of the remaining errors from the baseline Maxent model, achieving an F-score of 99.8227%.
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Aberdeen, J., Burger, J., Day, D., Hirschman, L., Robinson, P., Vilain, M.: Mitre: Description of the alembic system used for muc-6. In: Proceedings of the Sixth Message Understanding Conference (MUC-6), Columbia, Maryland. Morgan Kaufmann, San Francisco (1995)
Berger, A., Pietra, S.D., Pietra, V.D.: A maximum entropy approach to natural language processing. Computational linguistics 22(1), 39–71 (1996)
Della Pietra, S., Della Pietra, V., Lafferty, J.: Inducing features of random fields. Transactions Pattern Analysis and Machine Intelligence 19(4), 380–393 (1997)
Malouf, R.: A comparison of algorithms for maximum entropy parameter estimation. In: Proceedings of CoNLL-2002, Taipei, Taiwan, pp. 49–55 (2002)
Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of english: The penn treebank. Computational Linguistics 19(2), 313–329 (1993)
Mikheev, A.: Tagging sentence boundaries. In: Proceedings of the First Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL 2000 (2000)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Palmer, D.D., Hearst, M.A.: Adaptive Multilingual Sentence Boundary Disambiguation. Computational Linguistics 23(2), 241–267 (1997)
Ratnaparkhi, A.: Maximum entropy models for natural language ambiguity resolution. Ph.D. dissertation, University of Pennsylvania (1998)
Reynar, J.C., Ratnaparkhi, A.: A maximum entropy approach to identifying sentence boundaries. In: Proceedings of the Fifth Conference on Applied Natural Language Processing, Washington, D.C. (1997)
Riley, M.D.: Some applications of tree-based modelling to speech and language indexing. In: Proceedings of the DARPA Speech and Natural Language Workshop, pp. 339–352. Morgan Kaufmann (1989)
Rosenfeld, R.: Adaptive statistical language modeling: A Maximum Entropy Approach. PhD thesis CMU-CS-94 (1994)
Van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
Wallach, H.M.: Efficient training of conditional random fields. Master’s thesis, University of Edinburgh (2002)
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Kit, C., Liu, X. (2005). Period Disambiguation with Maxent Model. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_20
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DOI: https://doi.org/10.1007/11562214_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
Online ISBN: 978-3-540-31724-1
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