Grammar, interpretation, and processing from the lexicon
Lexical representation and process
Incremental Syntactic Parsing of Natural Language Corpora with Simple Synchrony Networks
IEEE Transactions on Knowledge and Data Engineering
Learning parse and translation decisions from examples with rich context
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Efficient probabilistic top-down and left-corner parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
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This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treebank data to learn first pass attachments, and is employed as a heuristic for guidingpa rsingde cision. The parser is lexically blind and uses beam search to explore the space of plausible partial parses and returns the full analysis havinghi ghest probability. Results are based on preliminary tests on the WSJ section of the Penn treebank and suggest that our incremental strategy is a computationally viable approach to parsing.