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

Transformation Rule Learning without Rule Templates: A Case Study in Part of Speech Tagging

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

4 Author(s)
Ngo Xuan Bach ; Coll. of Technol., Vietnam Nat. Univ., Hanoi ; Le Anh Cuong ; Nguyen Viet Ha ; Nguyen Ngoc Binh

Part of speech (POS) tagging is an important problem and is one of the first steps included in many tasks in natural language processing. It affects directly on the accuracy of many other problems such as Syntax Parsing, WordSense Disambiguation, and Machine Translation. Stochastic models solve this problem relatively well, but they still make mistakes. Transformation-based learning (TBL) is a solution which can be used to improve stochastic taggers by learning a set of transformation rules. However, its rule learning algorithm has the disadvantages that rule templates must be prepared by hand and only rules are instances of rule templates can be generated. In this paper, we propose a model to learn transformation rules without rule templates. This model considers the rule learning problem as a feature selection problem. Experiments on PennTree Bank showed that the proposal model reduces errors of stochastic taggers with some tags.

Published in:

Advanced Language Processing and Web Information Technology, 2008. ALPIT '08. International Conference on

Date of Conference:

23-25 July 2008

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.