Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
C4.5: programs for machine learning
C4.5: programs for machine learning
Information Retrieval
The Journal of Machine Learning Research
Learning rules and their exceptions
The Journal of Machine Learning Research
Restricted representation of phrase structure grammar for building a tree annotated corpus of Korean
Natural Language Engineering
Incremental finite-state parsing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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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.