Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55363
Title: Improving transfer learning in cross lingual opinion analysis through negative transfer detection
Authors: Gui, L
Lu, Q 
Xu, R
Wei, Q
Cao, Y
Keywords: Class noise detection
Negative transfer
Transfer learning
Issue Date: 2015
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Transfer learning has been used as a machine learning method to make good use of available language resources for other resource-scarce languages. However, the cumulative class noise during iterations of transfer learning can lead to negative transfer which can adversely affect performance when more training data is used. In this paper, we propose a novel transfer learning method which can detect negative transfers. This approach detects high quality samples after certain iterations to identify class noise in new transferred training samples and remove them to reduce misclassifications. With the ability to detect bad training samples and remove them, our method can make full use of large unlabeled training data available in the target language. Furthermore, the most important contribution in this paper is the theory of class noise detection. Our new class noise detection method overcame the theoretic flaw of a previous method based on Gaussian distribution. We applied this transfer learning method with negative transfer detection to cross lingual opinion analysis. Evaluation on the NLP&CC 2013 cross-lingual opinion analysis dataset shows that the proposed approach outperforms the state-of-the-art systems.
Description: 8th International Conference, KSEM 2015, Chongqing, China, October 28-30, 2015
URI: http://hdl.handle.net/10397/55363
ISBN: 9783319251585
9783319251585
9783319251585
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-25159-2_36
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

37
Last Week
5
Last month
Checked on Oct 16, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.