A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

Chengyu Wang, Xiaofeng He, Aoying Zhou


Abstract
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for long-tailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.
Anthology ID:
D17-1123
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1190–1203
Language:
URL:
https://www.aclweb.org/anthology/D17-1123
DOI:
10.18653/v1/D17-1123
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1123.pdf