C4.5: programs for machine learning
C4.5: programs for machine learning
Computational Linguistics
Learning and Inference for Clause Identification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Adaptive multilingual sentence boundary disambiguation
Computational Linguistics
Using decision trees to construct a practical parser
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A simple but useful approach to conjunct identification
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Automatic corpus-based Thai word extraction with the c4.5 learning algorithm
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Introduction to the CoNLL-2001 shared task: clause identification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
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In dependency parsing of long sentences with fewer subjects than predicates, it is difficult to recognize which predicate governs which subject. To handle such syntactic ambiguity between subjects and predicates, an “S(ubject)-clause” is defined as a group of words containing several predicates and their common subject, and then an automatic S-clause segmentation method is proposed using semantic features as well as morpheme features. We also propose a new dependency tree to reflect S-clauses. Trace information is used to indicate the omitted subject of each predicate. The S-clause information turned out to be very effective in analyzing long sentences, with an improved parsing performance of 4.5%. The precision in determining the governors of subjects in dependency parsing was improved by 32%.