@inproceedings{wang-etal-2017-short,
title = "A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances",
author = "Wang, Chengyu and
He, Xiaofeng and
Zhou, Aoying",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1123",
doi = "10.18653/v1/D17-1123",
pages = "1190--1203",
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.",
}
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%0 Conference Proceedings
%T A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
%A Wang, Chengyu
%A He, Xiaofeng
%A Zhou, Aoying
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-short
%X 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.
%R 10.18653/v1/D17-1123
%U https://aclanthology.org/D17-1123
%U https://doi.org/10.18653/v1/D17-1123
%P 1190-1203
Markdown (Informal)
[A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances](https://aclanthology.org/D17-1123) (Wang et al., EMNLP 2017)
ACL