Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/ios-press/combining-statistical-data-analysis-techniques-to-extract-topical-pcFNppbov4
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- Publisher
- IOS Press
- Copyright
- Copyright © 2005 by IOS Press, Inc
- ISSN
- 1088-467X
- eISSN
- 1571-4128
- Publisher site
- See Article on Publisher Site
Abstract
We present an unsupervised method for the generation from a textual corpus of sets of keywords, that is, words whose occurrences in a text are strongly connected with the presence of a given topic. Each of these classes is associated with one of the main topics of the corpus, and can be used to detect the presence of that topic in any of its paragraphs, by a simple keyword co-occurrence criterion. The classes are extracted from the textual data in a fully automatic way, without requiring any a priori linguistic knowledge or making any assumptions about the topics to search for. The algorithms we have developed allow us to yield satisfactory and directly usable results despite the amount of noise inherent in textual data. That goal is reached thanks to a combination of several data analysis techniques. On a corpus of archives from the French monthly newspaper Le Monde Diplomatique, we obtain 40 classes of about 30 words each that accurately characterize precise topics, and allow us to detect their occurrences with a precision and recall of 85% and 65% respectively.
Journal
Intelligent Data Analysis – IOS Press
Published: Jan 1, 2005