Automatic labeling of semantic roles
Computational Linguistics
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Using a smoothing maximum entropy model for chinese nominal entity tagging
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Robust word sense translation by EM learning of frame semantics
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Aligning features with sense distinction dimensions
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A comparative study on representing units in chinese text clustering
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
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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 results indicate that these lexical sets improve the model's performance for the three most challenging verbs chosen from the first set of experiments.