Abstract
The accuracy of statistical parsing models can be improved with the use of lexical information. Statistical parsing using Lexicalized tree adjoining grammar (LTAG), a kind of lexicalized grammar, has remained relatively unexplored. We believe that is largely in part due to the absence of large corpora accurately bracketed in terms of a perspicuous yet broad coverage LTAG. Our work attempts to alleviate this difficulty. We extract different LTAGs from the Penn Treebank. We show that certain strategies yield an improved extracted LTAG in terms of compactness, broad coverage, and supertagging accuracy. Furthermore, we perform a preliminary investigation in smoothing these grammars by means of an external linguistic resource, namely, the tree families of an XTAG grammar, a hand built grammar of English.- Anthology ID:
- 2000.iwpt-1.9
- Volume:
- Proceedings of the Sixth International Workshop on Parsing Technologies
- Month:
- February 23-25
- Year:
- 2000
- Address:
- Trento, Italy
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–76
- Language:
- URL:
- https://aclanthology.org/2000.iwpt-1.9
- DOI:
- Cite (ACL):
- John Chen and K. Vijay-Shanker. 2000. Automated Extraction of TAGs from the Penn Treebank. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 65–76, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Automated Extraction of TAGs from the Penn Treebank (Chen & Vijay-Shanker, IWPT 2000)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2000.iwpt-1.9.pdf