Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1434-1440.
Abstract
Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring concepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it performs inference based on clustering and the distributional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An empirical evaluation on five corpora demonstrates the utility of our proposed approach.
@inproceedings{ijcai2015p206,
title = {Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus},
author = {Alfarone, Daniele and Davis, Jesse},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1434--1440},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/206},
}
TY - CPAPER TI - Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus AU - Alfarone, Daniele AU - Davis, Jesse T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1434 EP - 1440 UR - https://ijcai.org/proceedings/2015/206 ER -Copied
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