by Viktor Pekar, Steffen Staab
Abstract:
The paper examines different possibilities to take advantage of the taxonomic organization of a thesaurus to improve the accuracy of classifying new words into its classes. The results of the study demonstrate that taxonomic similarity between nearest neighbors, in addition to their distributional similarity to the new word, may be useful evidence on which classification decision can be based.
Reference:
Taxonomy learning: factoring the structure of a taxonomy into a semantic classification decision (Viktor Pekar, Steffen Staab), In COLING'02 Proceedings of the 19th international conference on Computational linguistics, Association for Computational Linguistics, volume 2, 2002.
Bibtex Entry:
@inproceedings{Pekar2002,
abstract = {The paper examines different possibilities to take advantage of the taxonomic organization of a thesaurus to improve the accuracy of classifying new words into its classes. The results of the study demonstrate that taxonomic similarity between nearest neighbors, in addition to their distributional similarity to the new word, may be useful evidence on which classification decision can be based.},
author = {Pekar, Viktor and Staab, Steffen},
booktitle = {COLING'02 Proceedings of the 19th international conference on Computational linguistics},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {1--7},
publisher = {Association for Computational Linguistics},
title = {{Taxonomy learning: factoring the structure of a taxonomy into a semantic classification decision}},
volume = {2},
year = {2002}
}