Unsupervised Part-of-Speech Tagging Employing Efficient Graph Clustering

An unsupervised part-of-speech (POS) tagging system that relies on graph clustering methods is described. Unlike in current state-of-the-art approaches, the kind and number of different tags is generated by the method itself. We compute and merge two partitionings of word graphs: one based on context similarity of high frequency words, another on log-likelihood statistics for words of lower frequencies. Using the resulting word clusters as a lexicon, a Viterbi POS tagger is trained, which is refined by a morphological component. The approach is evaluated on three different languages by measuring agreement with existing taggers.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Chris Biemann
• Keywords: ACL 2006, ACL 2007, Current State-of-the-art Approaches, Graph Clustering Methods, Viterbi POS Tagger