by Sam Scott, Stan Matwin
Abstract:
This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypemyms. Six binary classification tasks of varying difficulty are defined, and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation. Rules are also produced with the commonly used bag-of-words representation, incorporating no knowledge from WordNet. Experiments show
Reference:
Text Classification Using WordNet Hypernyms (Sam Scott, Stan Matwin), In Learning (Sanda Harabagiu, ed.), Association for Computational Linguistics, 1998.
Bibtex Entry:
@article{Scott1998,
abstract = {This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypemyms. Six binary classification tasks of varying difficulty are defined, and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation. Rules are also produced with the commonly used bag-of-words representation, incorporating no knowledge from WordNet. Experiments show},
author = {Scott, Sam and Matwin, Stan},
editor = {Harabagiu, Sanda},
journal = {Learning},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {45--51},
publisher = {Association for Computational Linguistics},
title = {{Text Classification Using WordNet Hypernyms}},
url = {http://acl.ldc.upenn.edu/W/w98/W98-0706.pdf},
year = {1998}
}