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Phrase-based Document Categorization
| Citations: | 2 - 1 self |
BibTeX
@MISC{Koster_phrase-baseddocument,
author = {Cornelis H. A. Koster and Jean G. Beney and Suzan Verberne and Merijn Vogel},
title = {Phrase-based Document Categorization},
year = {}
}
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Abstract
takes a fresh look at an old idea in Information Retrieval: the use of linguistically extracted phrases as terms in the automatic categorization of documents, and in particular the pre-classification of patent applications. In Information Retrieval, until now there was found little or no evidence that document categorization benefits from the application of linguistics techniques. Classification algorithms using the most cleverly designed linguistic representations typically did not perform better than those using simply the bag-of-words representation. We have investigated the use of dependency triples as terms in document categorization, according to a dependency model based on the notion of aboutness and using normalizing transformations to enhance recall. We describe a number of large-scale experiments with different document representations, test collections and even languages, presenting evidence that adding such triples to the words in a bag-of-terms document representation may lead to a statistically significant increase in the accuracy of document categorization. 1
Keyphrases
phrase-based document categorization information retrieval document categorization dependency model significant increase bag-of-terms document representation normalizing transformation document categorization benefit linguistics technique large-scale experiment old idea patent application linguistic representation dependency triple automatic categorization different document representation fresh look bag-of-words representation test collection
