Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation

• URL: http://arxiv.org/abs/2211.01981v1
• Date: Tue, 18 Oct 2022 22:38:49 GMT
• Title: Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
• Authors: Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han
• Abstract summary: We propose a novel framework for topic taxonomy expansion, named TopicExpan. TopicExpan directly generates topic-related terms belonging to new topics. Experimental results on two real-world text corpora show that TopicExpan significantly outperforms other baseline methods in terms of the quality of output.
• Score: 58.3921103230647
• License: http://creativecommons.org/licenses/by/4.0/
• 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 topic hierarchy. 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.

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