Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
This work addresses the challenge of constructing accurate hierarchical topic structures for natural language processing applications, representing an incremental improvement over prior methods.
The paper tackled the problem of low-quality topics and inaccurate hierarchical relations in topic taxonomy discovery by developing BoxTM, a model that maps words and topics into a box embedding space with an asymmetric metric, resulting in validated high-quality topic taxonomies.
Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them. Unfortunately, most of prior work can hardly model semantic scopes of words and topics by holding the Euclidean embedding space assumption. What's worse, they infer asymmetric hierarchical relations by symmetric distances between topic embeddings. As a result, existing methods suffer from problems of low-quality topics at high abstraction levels and inaccurate hierarchical relations. To alleviate these problems, this paper develops a Box embedding-based Topic Model (BoxTM) that maps words and topics into the box embedding space, where the asymmetric metric is defined to properly infer hierarchical relations among topics. Additionally, our BoxTM explicitly infers upper-level topics based on correlation between specific topics through recursive clustering on topic boxes. Finally, extensive experiments validate high-quality of the topic taxonomy learned by BoxTM.