Paper: Unsupervised Learning Of Word-Category Guessing Rules

ACL ID P96-1043
Title Unsupervised Learning Of Word-Category Guessing Rules
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1996
Authors

Words unknown to the lexicon present a substantial problem to part-of-speech tag- ging. In this paper we present a technique for fully unsupervised statistical acquisi- tion of rules which guess possible parts- of-speech for unknown words. Three com- plementary sets of word-guessing rules are induced from the lexicon and a raw cor- pus: prefix morphological rules, suffix mor- phological rules and ending-guessing rules. The learning was performed on the Brown Corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art.