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Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
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ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
An analysis of tree topological features in classifier-based unlexicalized parsing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
A text-based decision support system for financial sequence prediction
Decision Support Systems
Characterizing stylistic elements in syntactic structure
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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As unlexicalized parsing lacks word token information, it is important to investigate novel parsing features to improve the accuracy. This paper studies a set of tree topological (TT) features. They quantitatively describe the tree shape dominated by each non-terminal node. The features are useful in capturing linguistic notions such as grammatical weight and syntactic branching, which are factors important to syntactic processing but overlooked in the parsing literature. By using an ensemble classifier-based model, TT features can significantly improve the parsing accuracy of our unlexicalized parser. Further, the ease of estimating TT feature values makes them easy to be incorporated into virtually any mainstream parsers.