TaxoGen: Constructing Topical Concept Taxonomy By Adaptive Term Embedding And Clustering

Authors:
Chao Zhang University of Illinois at Urbana-Champaign
Fangbo Tao Facebook
Xiusi Chen University of Illinois at Urbana-Champaign
Jiaming Shen University of Illinois at Urbana-Champaign
Meng Jiang University of Notre Dame
Brian Sadler U.S. Army Research Lab
Michelle Vanni U.S. Army Research Lab
Jiawei Han University of Illinois at Urbana-Champaign

Introduction:

This paper studies Taxonomy construction. The authors propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms.

Abstract:

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they overlook the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.

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