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:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W03-3006.pdf