Supertagging: A Non-Statistical Parsing-Based Approach

Pierre Boullier


Abstract
We present a novel approach to supertagging w.r.t. some lexicalized grammar G. It differs from previous approaches in several ways:- These supertaggers rely only on structural information: they do not need any training phase;- These supertaggers do not compute the “best“ supertag for each word, but rather a set of supertags. These sets of supertags do not exclude any supertag that will eventually be used in a valid complete derivation (i.e., we have a recall score of 100%);- These supertaggers are in fact true parsers which accept supersets of L(G) that can be more efficiently parsed than the sentences of L(G).
Anthology ID:
W03-3006
Volume:
Proceedings of the Eighth International Conference on Parsing Technologies
Month:
April
Year:
2003
Address:
Nancy, France
Venue:
IWPT
SIG:
SIGPARSE
Publisher:
Note:
Pages:
55–65
Language:
URL:
https://aclanthology.org/W03-3006
DOI:
Bibkey:
Cite (ACL):
Pierre Boullier. 2003. Supertagging: A Non-Statistical Parsing-Based Approach. In Proceedings of the Eighth International Conference on Parsing Technologies, pages 55–65, Nancy, France.
Cite (Informal):
Supertagging: A Non-Statistical Parsing-Based Approach (Boullier, IWPT 2003)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W03-3006.pdf