Using Compression to Identify Acronyms in Text (2001)
| Venue: | Proc Data Compression Conference, edited by J.A. Storer and M. Cohn. IEEE Press Los Alamitos, CA |
| Citations: | 13 - 6 self |
BibTeX
@INPROCEEDINGS{Yeates01usingcompression,
author = {Stuart Yeates and David Bainbridge and Ian H. Witten},
title = {Using Compression to Identify Acronyms in Text},
booktitle = {Proc Data Compression Conference, edited by J.A. Storer and M. Cohn. IEEE Press Los Alamitos, CA},
year = {2001},
pages = {582}
}
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Abstract
Introduction Text mining is about looking for patterns in natural language text, and may be dened as the process of analyzing text to extract information from it for particular purposes. In previous work, we claimed that compression is a key technology for text mining, and backed this up with a study that showed how particular kinds of lexical tokens|names, dates, locations, etc.|can be identied and located in running text, using compression models to provide the leverage necessary to distinguish dierent token types (Witten et al., 1999). Identifying acronyms in documents|which is certainly also about looking for patterns in text|presents a rather dierent kind of problem. Webster denes an \acronym" as a word formed from the rst (or rst few) letters of a series of words, as radar , from radio detecting and ranging. Acronyms are often dened by preceding (or follow
Citations
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| 2215 | Data Mining: Practical machine learning tools and techniques. 2nd edition - Witten, Frank - 2005 |
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| 28 | Text mining: a new frontier for lossless compression - Witten, Bray, et al. - 1999 |
| 11 | The Design and Analysis of Ecient Lossless Data Compression Systems - Howard - 1993 |
| 4 | Recognizing Acronyms and their De - Taghva, Gilbreth - 1995 |
