Unsupervised Learning of Word-Category Guessing Rules

Words unknown to the lexicon present a substantial problem to part-of-speech tagging. In this paper we present a technique for fully unsupervised statistical acquisition of rules which guess possible partsof-speech for unknown words. Three complementary sets of word-guessing rules are induced from the lexicon and a raw corpus: prefix morphological rules, suffix morphological 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.

• Type: Conference
• Year: 1996
• Where: ACL
• Authors: Andrei Mikheev
• Keywords: ACL 1996, ACL 2007, Morphological Rules, Prefix Morphological Rules, Unsupervised Statistical Acquisition