Abstract
Partial parsing techniques try to recover syntactic information efficiently and reliably by sacrificing completeness and depth of analysis. One of the difficulties of partial parsing is finding a means to extract the grammar involved automatically. In this paper, we present a method for automatically extracting partial parsing rules from a tree-annotated corpus using decision tree induction. We define the partial parsing rules as those that can decide the structure of a substring in an input sentence deterministically. This decision can be considered as a classification; as such, for a substring in an input sentence, a proper structure is chosen among the structures occurred in the corpus. For the classification, we use decision tree induction, and induce partial parsing rules from the decision tree. The acquired grammar is similar to a phrase structure grammar, with contextual and lexical information, but it allows building structures of depth one or more. Our experiments showed that the proposed partial parser using the automatically extracted rules is not only accurate and efficient, but also achieves reasonable coverage for Korean.
This research was supported in part by the Ministry of Science and Technology, the Ministry of Culture and Tourism, and the Korea Science and Engineering Foundation in Korea.
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Choi, MS., Lim, C.S., Choi, KS. (2005). Automatic Partial Parsing Rule Acquisition Using Decision Tree Induction. 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_13
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DOI: https://doi.org/10.1007/11562214_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
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