A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Discovery of linguistic relations using lexical attraction
Discovery of linguistic relations using lexical attraction
Introduction to the special issue on computational linguistics using large corpora
Computational Linguistics - Special issue on using large corpora: I
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Word association and MI-Trigger-based language modeling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
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This paper presents an approach to build a novel two-level collocation net, which enables calculation of the collocation relationship between any two words, from a large raw corpus. The first level consists of atomic classes (each atomic class consists of one word and feature bigram), which are clustered into the second level class set. Each class in both levels is represented by its collocation candidate distribution, extracted from the linguistic analysis of the raw training corpus, over possible collocation relation types. In this way, all the information extracted from the linguistic analysis is kept in the collocation net. Our approach applies to both frequently and less-frequently occurring words by providing a clustering mechanism resolve the data sparseness problem through the collocation net. Experimentation shows that the collocation net is efficient and effective in solving the data sparseness problem and determining the collocation relationship between any two words.