  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.
