@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}