Multi-view clustering with web and linguistic features for relation extraction

Yulan Yan; Haibo Li; Yutaka Matsuo; Zhenglu Yang; Mitsuru Ishizuka

doi:10.1109/APWeb.2010.64

Multi-view clustering with web and linguistic features for relation extraction

Yulan Yan^*, Haibo Li, Yutaka Matsuo, Zhenglu Yang, Mitsuru Ishizuka

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.

Original language	English
Title of host publication	Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010
Pages	140-146
Number of pages	7
DOIs	https://doi.org/10.1109/APWeb.2010.64
Publication status	Published - 2010
Externally published	Yes
Event	12th International Asia Pacific Web Conference, APWeb 2010 - Busan Duration: 2010 Apr 6 → 2010 Apr 8

Other

Other	12th International Asia Pacific Web Conference, APWeb 2010
City	Busan
Period	10/4/6 → 10/4/8

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications

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10.1109/APWeb.2010.64

Cite this

Yan, Y, Li, H, Matsuo, Y, Yang, Z & Ishizuka, M 2010, Multi-view clustering with web and linguistic features for relation extraction. in Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010., 5474142, pp. 140-146, 12th International Asia Pacific Web Conference, APWeb 2010, Busan, 10/4/6. https://doi.org/10.1109/APWeb.2010.64

@inproceedings{10bceb263d1c4c0fa7cdba241a1622f7,

title = "Multi-view clustering with web and linguistic features for relation extraction",

abstract = "Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.",

author = "Yulan Yan and Haibo Li and Yutaka Matsuo and Zhenglu Yang and Mitsuru Ishizuka",

year = "2010",

doi = "10.1109/APWeb.2010.64",

language = "English",

isbn = "9780769540122",

pages = "140--146",

booktitle = "Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010",

note = "12th International Asia Pacific Web Conference, APWeb 2010 ; Conference date: 06-04-2010 Through 08-04-2010",

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TY - GEN

T1 - Multi-view clustering with web and linguistic features for relation extraction

AU - Yan, Yulan

AU - Li, Haibo

AU - Matsuo, Yutaka

AU - Yang, Zhenglu

AU - Ishizuka, Mitsuru

PY - 2010

Y1 - 2010

N2 - Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.

AB - Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.

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U2 - 10.1109/APWeb.2010.64

DO - 10.1109/APWeb.2010.64

M3 - Conference contribution

AN - SCOPUS:77954251938

SN - 9780769540122

SP - 140

EP - 146

BT - Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010

T2 - 12th International Asia Pacific Web Conference, APWeb 2010

Y2 - 6 April 2010 through 8 April 2010

ER -

Multi-view clustering with web and linguistic features for relation extraction

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