TY - GEN
T1 - TaxoGen
T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
AU - Zhang, Chao
AU - Tao, Fangbo
AU - Chen, Xiusi
AU - Shen, Jiaming
AU - Jiang, Meng
AU - Sadler, Brian
AU - Vanni, Michelle
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85051525930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051525930&partnerID=8YFLogxK
U2 - 10.1145/3219819.3220064
DO - 10.1145/3219819.3220064
M3 - Conference contribution
AN - SCOPUS:85051525930
SN - 9781450355520
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2701
EP - 2709
BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 19 August 2018 through 23 August 2018
ER -