Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han · 2022
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 Topic-Expan, 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.