The Journal of Machine Learning Research
Supertagging: an approach to almost parsing
Computational Linguistics
A SNoW based supertagger with application to NP chunking
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Use of deep linguistic features for the recognition and labeling of semantic arguments
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Efficient HPSG parsing with supertagging and CFG-filtering
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A simple approach for HPSG supertagging using dependency information
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Faster parsing by supertagger adaptation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Forest-guided supertagger training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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Supertagging is a widely used speed-up technique for deep parsing. In another aspect, supertagging has been exploited in other NLP tasks than parsing for utilizing the rich syntactic information given by the supertags. However, the performance of supertagger is still a bottleneck for such applications. In this paper, we investigated the relationship between supertagging and parsing, not just to speed up the deep parser; We started from a sequence labeling view of HPSG supertagging, examining how well a supertagger can do when separated from parsing. Comparison of two types of supertagging model, point-wise model and sequential model, showed that the former model works competitively well despite its simplicity, which indicates the true dependency among supertag assignments is far more complex than the crude first-order approximation made in the sequential model. We then analyzed the limitation of separated supertagging by using a CFG-filter. The results showed that big gains could be acquired by resorting to a light-weight parser.