Paper Info
Title
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Abstract
ABSTRACTTopic 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.
Year
DOI
Venue
2022
10.1145/3485447.3512002
International World Wide Web Conference
Keywords
DocType
Citations 
Topic taxonomy completion, Hierarchical topic discovery, Novelty detection, Text embedding, Text clustering
Conference
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Dongha Lee1146.77
Jiaming Shen2569.05
SeongKu Kang3214.55
Susik Yoon400.34
Jiawei Han5430853824.48
Hwanjo Yu61715114.02