Learning semantic categories from clickthrough logs

Authors:
Mamoru Komachi;Shimpei Makimoto;Kei Uchiumi;Manabu Sassano
Affiliations:
Nara Institute of Science and Technology (NAIST), Ikoma, Nara, Japan;Yahoo Japan Corporation, Minato-ku, Tokyo, Japan;Yahoo Japan Corporation, Minato-ku, Tokyo, Japan;Yahoo Japan Corporation, Minato-ku, Tokyo, Japan
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

Citing 4
Cited 2

Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Named entity mining from click-through data using weakly supervised latent dirichlet allocation

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Weakly-supervised acquisition of labeled class instances using graph random walks

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
What you seek is what you get: extraction of class attributes from query logs

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries

HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
The role of query sessions in extracting instance attributes from web search queries

ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

As the web grows larger, knowledge acquisition from the web has gained increasing attention. In this paper, we propose using web search clickthrough logs to learn semantic categories. Experimental results show that the proposed method greatly outperforms previous work using only web search query logs.