Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han
Abstract
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic information, several recent studies have tried to expand (or complete) a topic taxonomy by inserting emerging topics identified in a set of new documents. However, existing methods focus only on frequent terms in documents and the local topic-subtopic relations in a taxonomy, which leads to limited topic term coverage and fails to model the global taxonomy structure. In this work, we propose a novel framework for topic taxonomy expansion, named TopicExpan, which directly generates topic-related terms belonging to new topics. Specifically, TopicExpan leverages the hierarchical relation structure surrounding a new topic and the textual content of an input document for topic term generation. This approach encourages newly-inserted topics to further cover important but less frequent terms as well as to keep their relation consistency within the taxonomy. Experimental results on two real-world text corpora show that TopicExpan significantly outperforms other baseline methods in terms of the quality of output taxonomies.- Anthology ID:
- 2022.findings-emnlp.122
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1687–1700
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.122
- DOI:
- 10.18653/v1/2022.findings-emnlp.122
- Cite (ACL):
- Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, and Jiawei Han. 2022. Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1687–1700, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation (Lee et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.findings-emnlp.122.pdf