DMCA
Phrase-based Document Categorization revisited
| Citations: | 17 - 5 self |
BibTeX
@MISC{Koster_phrase-baseddocument,
author = {Cornelis H. A. Koster and Jean G. Beney},
title = {Phrase-based Document Categorization revisited},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
This paper takes a fresh look at an old idea in Information Retrieval: the use of linguistically extracted phrases as terms in the automatic categorization (aka classification) of documents. 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 linguistical representations typically do no better than those using simply the bag-of-words representation. Shallow linguistical techniques are used routinely, but their positive effect on the accuracy is small at best. We have investigated the use of dependency triples as terms in document categorization, which are derived according to a dependency model based on the notion of aboutness. The documents are syntactically analyzed by a parser and transduced to dependency graphs, which in turn are unnested into dependency triples following the aboutnessbased model. In the process, various normalizing transformations are applied to enhance recall. We describe a sequence 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 significant increase in the accuracy of document categorization.
Keyphrases
phrase-based document categorization dependency triple document categorization linguistics technique automatic categorization information retrieval positive effect document categorization benefit bag-of-words representation test collection dependency model old idea aboutnessbased model shallow linguistical technique bag-of-terms document representation linguistical representation large-scale experiment different document representation various normalizing transformation aka classification fresh look significant increase
