Cart (Loading....) | Create Account
Close category search window

A relation extraction method of Chinese named entities based on location and semantic features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Hai-Guang Li ; Sch. of Comput. Sci. & Inf. Eng., Hefei Univ. of Technol., Hefei, China ; Gong-Qing Wu ; Xue-Gang Hu ; Xindong Wu
more authors

Named entity relations are a foundation of semantic networks, ontology and the semantic Web, and are widely used in information retrieval and machine translation, as well as automatic question and answering systems. Relation feature selection and extraction are two key issues. The location features possess excellent computability and operability, and the semantic features have strong intelligibility and reality. Currently, relation extraction of Chinese named entities mainly adopts the vector space model (VSM) or a traditional semantic computing method, and these two methods use either the location features or the semantic features only, resulting in unsatisfactory extraction. To improve the extraction results, we propose a method that combines the information gain of the positions of words and the semantic computing based on HowNet to extract Chinese named entity relations, and present a relation extraction method of Chinese named entities, called LSE, which is scalable, semi-supervised and domain independent. Extensive experiments have been performed to show that our approach is superior, with an F-score of 0.881, which is at least 0.115 better than existing extraction methods that use either the location features or the semantic features.

Published in:

Granular Computing, 2009, GRC '09. IEEE International Conference on

Date of Conference:

17-19 Aug. 2009

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.