A short survey on taxonomy learning from text corpora: Issues, resources and recent advances

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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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Wang, C., He, X., & Zhou, A. (2017). A short survey on taxonomy learning from text corpora: Issues, resources and recent advances. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1190–1203). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1123

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