Selforganizing Classification on the New Reuters News Corpus
Proceedings of the International Conference on Computational Linguistics
pages 1086--1092,
- Aug 2002
Associated documents :
In this paper we propose an integration of a
selforganizing map and semantic networks
from WordNet for a text classification task
using the new Reuters news corpus. This
neural model is based on significance vectors
and benefits from the presentation of
document clusters. The Hypernym relation
in WordNet supplements the neural model in
classification. We also analyse the
relationships of news headlines and their
contents of the new Reuters corpus by a
series of experiments. This hybrid approach
of neural selforganization and symbolic
hypernym relationships is successful to
achieve good classification rates on 100,000
full-text news articles. These results
demonstrate that this approach can scale up
to a large real-world task and show a lot of
potential for text classification.
@InProceedings{WH02,
author = {Wermter, Stefan and Hung, Chihli},
title = {Selforganizing Classification on the New Reuters News Corpus},
booktitle = {Proceedings of the International Conference on Computational Linguistics},
number = {},
volume = {},
pages = {1086--1092},
year = {2002},
month = {Aug},
publisher = {},
doi = {},
}