Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task.
|Title of host publication||WWW 2022 - Proceedings of the ACM Web Conference 2022|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|Publication status||Published - 2022 Apr 25|
|Event||31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France|
Duration: 2022 Apr 25 → 2022 Apr 29
|Name||WWW 2022 - Proceedings of the ACM Web Conference 2022|
|Conference||31st ACM World Wide Web Conference, WWW 2022|
|Period||22/4/25 → 22/4/29|
Bibliographical notePublisher Copyright:
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications