Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Supersense tagging of unknown nouns in WordNet
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Supersense tagging of unknown nouns using semantic similarity
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Graph-based word clustering using a web search engine
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Combining contextual and structural information for supersense tagging of chinese unknown words
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
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Many NLP and IR applications require semantic classification knowledge of words. However, manually constructing semantic classes is a time-consuming and labor-intensive task. In this paper, we present an algorithm for induction of Chinese semantic classes from natural language text based on coordinate patterns. First, several coordinate patterns are proposed to harvest high-quality coordinate instance. Second, an iterative clustering process is used to cluster words into semantic classes. The clustering process mainly used coordinate relation between words. Experiment results show that the proposed approach performs relatively well and achieves 53.2% in terms of precision. Finally, a thesaurus containing about 15000 Chinese words is generated automatically.