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    <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.</abstract>
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%0 Conference Proceedings
%T Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
%A Lee, Dongha
%A Shen, Jiaming
%A Lee, Seonghyeon
%A Yoon, Susik
%A Yu, Hwanjo
%A Han, Jiawei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F lee-etal-2022-topic
%X 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.
%R 10.18653/v1/2022.findings-emnlp.122
%U https://aclanthology.org/2022.findings-emnlp.122
%U https://doi.org/10.18653/v1/2022.findings-emnlp.122
%P 1687-1700

Markdown (Informal)

[Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://aclanthology.org/2022.findings-emnlp.122) (Lee et al., Findings 2022)

• Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation (Lee et al., Findings 2022)

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.