Performance Evaluation of Supertagging for Partial Parsing

B. Srinivas


Abstract
In previous work we introduced the idea of supertagging as a means of improving the efficiency of a lexicalized grammar parser. In this paper, we present supertagging in conjunction with a lightweight dependency analyzer as a robust and efficient partial parser. The present work is significant for two reasons. First, we have vastly improved our results; 92% accurate for supertag disambiguation using lexical information, larger training corpus and smoothing techniques. Second, we show how supertagging can be used for partial parsing and provide detailed evaluation results for detecting noun chunks, verb chunks, preposition phrase attachment and a variety of other linguistic constructions. Using supertag representation, we achieve a recall rate of 93.0% and a precision rate of 91.8% for noun chunking, improving on the best known result for noun chunking.
Anthology ID:
1997.iwpt-1.22
Volume:
Proceedings of the Fifth International Workshop on Parsing Technologies
Month:
September 17-20
Year:
1997
Address:
Boston/Cambridge, Massachusetts, USA
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
187–198
Language:
URL:
https://aclanthology.org/1997.iwpt-1.22
DOI:
Bibkey:
Cite (ACL):
B. Srinivas. 1997. Performance Evaluation of Supertagging for Partial Parsing. In Proceedings of the Fifth International Workshop on Parsing Technologies, pages 187–198, Boston/Cambridge, Massachusetts, USA. Association for Computational Linguistics.
Cite (Informal):
Performance Evaluation of Supertagging for Partial Parsing (Srinivas, IWPT 1997)
Copy Citation:
PDF:
https://aclanthology.org/1997.iwpt-1.22.pdf