Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
This work addresses the need for dynamic topic incorporation in NLP applications, offering an incremental improvement over prior methods by better modeling global hierarchy and term coverage.
The paper tackled the problem of expanding topic taxonomies by generating topic-related terms for new topics, addressing limitations in existing methods that focus on frequent terms and local relations. It resulted in TopicExpan, a framework that significantly outperformed baseline methods in taxonomy quality on two real-world corpora.
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.