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Abstract: This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hun- dred words, we show that their influence is nonstationary on a much smaller time scale. Empirical data drawn from English and Japanese text, as well as conversational speech,' reveals that the "attraction " between words decays ... (Update)
Context of citations to this paper: More
.... use linguistic criteria such as cue phrases, punctuation marks, prosodic features, reference, syntax and lexical attraction [1, 2, 3, 15, 24, 25]. Another approach utilizes statistical similarity measures such as word cooccurrence (according to Halliday and Hasan [8]...
.... by Beeferman et al. uses n grams within a language model, but does not explicitly track the frequency of multiple word phrases [Beeferman et al. 1997b] The number of repeated bigrams in text is smaller than the number of repeated words and the number of trigrams which...
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BibTeX entry: (Update)
D. Beeferman, A. Berger, and J. Lafferty, A model of lexical attraction and repulsion, In Proceedings of the ACL, Madrid, Spain, 1997. http://citeseer.ist.psu.edu/beeferman97model.html More
@inproceedings{ beeferman97model,
author = "Doug Beeferman and Adam Berger and John Lafferty",
title = "A Model of Lexical Attraction and Repulsion",
booktitle = "Proceedings of the Thirty-Fifth Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics and Eighth Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics",
publisher = "Association for Computational Linguistics",
address = "Somerset, New Jersey",
editor = "Philip R. Cohen and Wolfgang Wahlster",
pages = "373--380",
year = "1997",
url = "citeseer.ist.psu.edu/beeferman97model.html" }
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