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://aclanthology.org/D17-1123
- DOI:
- 10.18653/v1/D17-1123
- Cite (ACL):
- Chengyu Wang, Xiaofeng He, and Aoying Zhou. 2017. A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1190–1203, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances (Wang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-1123.pdf
- Data
- YAGO