Supertagging: an approach to almost parsing
Computational Linguistics
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EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
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Computational linguistics and natural language processing
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
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Supertagging is an important technique for deep syntactic analysis. A supertagger is usually trained independently of the parser using a sequence labeling method. This presents an inconsistent training objective between the supertagger and the parser. In this paper, we propose a forest-guided supertagger training method to alleviate this problem by incorporating global grammar constraints into the supertagging process using a CFG-filter. It also provides an approach to make the supertagger and the parser more tightly integrated. The experiment shows that using the forest-guided trained supertagger, the parser got an absolute 0.68% improvement from baseline in F-score for predicate-argument relation recognition accuracy and achieved a competitive result of 89.31% with a faster parsing speed, compared to a state-of-the-art HPSG parser.