Text Segmentation based on Document Understanding for Information Retrieval

Abstract : Information retrieval needs to match relevant texts with a given query. Selecting appropriate parts is useful when documents are long, and only portions are interesting to the user. In this paper, we describe a method that extensively uses natural language techniques for text segmentation based on topic change detection. The method requires a NLP-parser and a semantic representation in Roget-based vectors. We have run the experiment on French documents, for which we have the appropriate tools, but the method could be transposed to any other lan- guage with the same requirements. The article sketches an overview of the NL understanding environment functionalities, and the algorithms related to our text segmentation method. An experiment in text seg- mentation is also presented and its result in an information retrieval task is shown.
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Communication dans un congrès
NLDB'07, Jun 2007, pp.295-304, 2007
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00161996
Contributeur : Alexandre Labadié <>
Soumis le : jeudi 12 juillet 2007 - 10:31:42
Dernière modification le : jeudi 11 janvier 2018 - 06:26:53
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Violaine Prince, Alexandre Labadié. Text Segmentation based on Document Understanding for Information Retrieval. NLDB'07, Jun 2007, pp.295-304, 2007. 〈lirmm-00161996〉

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