Paper: LiLFeS - Towards a Practical HPSG Parser

ACL ID P98-2132
Title LiLFeS - Towards a Practical HPSG Parser
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998

This paper presents a multi-neuro tagger that uses variable lengths of contexts and weighted inputs (with information gains) for part of speech tagging. Computer experiments show that it has a correct rate of over 94% for tag- ging ambiguous words when a small Thai corpus with 22,311 ambiguous words is used for train- ing. This result is better than any of the results obtained using the single-neuro taggers with fixed but different lengths of contexts, which indicates that the multi-neuro tagger can dy- namically find a suitable length of contexts in tagging.