By Topic

Auto-POS templates and mixed metrics for recognizing terms in scientific literature

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

5 Author(s)
Hongliang You ; Sch. of Electr. & Inf. Eng., Xi''an Jiaotong Univ., Xi''an, China ; Wei Zhang ; Junyi Shen ; Yang Yu
more authors

Automatic Term Recognition (ATR) is an important task for Knowledge Acquisition, which aims at acquiring formalized words which are not recorded in time in the glossary. In recent years, several statistical methods has proved to be effective, and emerging methods such as C-value, NC-Value, TermExtractor has shown great advantages on this task. However, few works have been done on the Metric mixing algorithm that combines those metrics as a whole. In this paper, we first collect part-of-speech templates from already-known terms automatically, namely Auto-POS templates, instead of artificial regular expressions, and then we match them with POS strings to acquire candidate terms. Finally we sort those candidates by metric mixing algorithm. Experimental results on IEEE2006-2007 metadata show that the metric mixing algorithm performs better than any separate metrics alone.

Published in:

Knowledge Acquisition and Modeling (KAM), 2010 3rd International Symposium on

Date of Conference:

20-21 Oct. 2010