Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

Jie Huang, Kevin Chang, JinJun Xiong, Wen-mei Hwu


Abstract
We propose to measure fine-grained domain relevance– the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle long-tail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain. To reduce expensive human efforts, we employ automatic annotation and hierarchical positive-unlabeled learning. Our approach applies to big or small domains, covers head or tail terms, and requires little human effort. Extensive experiments demonstrate that our methods outperform strong baselines and even surpass professional human performance.
Anthology ID:
2021.acl-long.282
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3641–3651
Language:
URL:
https://aclanthology.org/2021.acl-long.282
DOI:
10.18653/v1/2021.acl-long.282
Bibkey:
Cite (ACL):
Jie Huang, Kevin Chang, JinJun Xiong, and Wen-mei Hwu. 2021. Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3641–3651, Online. Association for Computational Linguistics.
Cite (Informal):
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach (Huang et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.282.pdf
Video:
 https://aclanthology.org/2021.acl-long.282.mp4
Code
 jeffhj/domain-relevance