Another look at automatic text-retrieval systems
Communications of the ACM
Word association norms, mutual information, and lexicography
Computational Linguistics
On the use of term associations in automatic information retrieval
COLING '86 Proceedings of the 11th coference on Computational linguistics
Collocational analysis in Japanese text input
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Dialogue act tagging with Transformation-Based Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An NTU-approach to automatic sentence extraction for summary generation
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Identifying document topics using the Wikipedia category network
Web Intelligence and Agent Systems
Topic-Dependent Language Model with Voting on Noun History
ACM Transactions on Asian Language Information Processing (TALIP)
Enhancing biomedical concept extraction using semantic relationship weights
International Journal of Data Mining and Bioinformatics
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This paper proposes a corpus-based language model for topic identification. We analyze the association of noun-noun and noun-verb pairs in LOB Corpus. The word association norms are based on three factors: 1) word importance, 2) pair co-occurrence, and 3) distance. They are trained on the paragraph and sentence levels for noun-noun and noun-verb pairs, respectively. Under the topic coherence postulation, the nouns that have the strongest connectivities with the other nouns and verbs in the discourse form the preferred topic set. The collocational semantics then is used to identify the topics from paragraphs and to discuss the topic shift phenomenon among paragraphs.