Paper: Efficient Unsupervised Discovery Of Word Categories Using Symmetric Patterns And High Frequency Words

ACL ID P06-1038
Title Efficient Unsupervised Discovery Of Word Categories Using Symmetric Patterns And High Frequency Words
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We present a novel approach for discov- ering word categories, sets of words shar- ing a significant aspect of their mean- ing. We utilize meta-patterns of high- frequency words and content words in or- der to discover pattern candidates. Sym- metric patterns are then identified using graph-based measures, and word cate- gories are created based on graph clique sets. Our method is the first pattern-based method that requires no corpus annota- tion or manually provided seed patterns or words. We evaluate our algorithm on very large corpora in two languages, us- ing both human judgments and WordNet- based evaluation. Our fully unsupervised results are superior to previous work that used a POS tagged corpus, and computa- tion time for huge corpora are orders of magnitude faster than previousl...