Handbook of formal languages, vol. 3
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic grammar generation from two different perspectives
Automatic grammar generation from two different perspectives
Towards efficient statistical parsing using lexicalized grammatical information
Towards efficient statistical parsing using lexicalized grammatical information
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Supertagging: an approach to almost parsing
Computational Linguistics
New models for improving supertag disambiguation
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Disambiguation of super parts of speech (or supertags): almost parsing
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Text chunking using regularized Winnow
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data & Knowledge Engineering - Special jubilee issue: DKE 50
Chinese word segmentation as LMR tagging
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
HPSG supertagging: a sequence labeling view
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
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
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 the tagging process of assigning the correct elementary tree of LTAG, or the correct supertag, to each word of an input sentence. In this paper we propose to use supertags to expose syntactic dependencies which are unavailable with POS tags. We first propose a novel method of applying Sparse Network of Winnow (SNoW) to sequential models. Then we use it to construct a supertagger that uses long distance syntactical dependencies, and the supertagger achieves an accuracy of 92.41%. We apply the supertagger to NP chunking. The use of supertags in NP chunking gives rise to almost 1% absolute increase (from 92.03% to 92.95%) in F-score under Transformation Based Learning(TBL) frame. The surpertagger described here provides an effective and efficient way to exploit syntactic information.