Paper: Chinese Verb Sense Discrimination Using An EM Clustering Model With Rich Linguistic Features

ACL ID P04-1038
Title Chinese Verb Sense Discrimination Using An EM Clustering Model With Rich Linguistic Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper discusses the application of the Expectation-Maximization (EM) clustering algorithm to the task of Chinese verb sense discrimination. The model utilized rich linguistic features that capture predicate- argument structure information of the target verbs. A semantic taxonomy for Chinese nouns, which was built semi-automatically based on two electronic Chinese semantic dictionaries, was used to provide semantic features for the model. Purity and normalized mutual information were used to evaluate the clustering performance on 12 Chinese verbs. The experimental results show that the EM clustering model can learn sense or sense group distinctions for most of the verbs successfully. We further enhanced the model with certain fine-grained semantic categories called lexical sets. Our re...