Improving statistical parsing by linguistic regularization
Name
Berwick-Improving Statistical parsing.pdf
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892.79 KB
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Checksum (MD5)
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Author(s)
Berwick, Robert C.
Malioutov, Igor Mikhailovich
Date Issued
January 2011
Journal
10th International Conference on Intelligent Systems Design and Applications, 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Malioutov, Igor, and Robert C. Berwick. “Improving Statistical Parsing by Linguistic Regularization.” IEEE, 2010. 1071–1076. © Copyright 2010 IEEE
Version
Final published version
Abstract
Statistically-based parsers for large corpora, in particular the Penn Tree Bank (PTB), typically have not used all the linguistic information encoded in the annotated trees on which they are trained. In particular, they have not in general used information that records the effects of derivations, such as empty categories and the representation of displaced phrases, as is the case with passive, topicalization, and wh-constructions. Here we explore ways to use this information to “unwind” derivations, yielding a regularized underlying syntactic structure that can be used as an additional source of information for more accurate parsing. In effect, we make use of two joint sets of tree structures for parsing: the surface structure and its corresponding underlying structure where arguments have been restored to their canonical positions. We present a pilot experiment on passives in the PTB indicating that through the use of these two syntactic representations we can improve overall parsing performance by exploiting transformational regularities, in this way paring down the search space of possible syntactic analyses.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ISDA.2010.5687043
