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
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Note:
Pages:
55–65
Language:
URL:
https://www.aclweb.org/anthology/W03-3006
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W03-3006.pdf