Cross-lingual sentiment classification with stacked autoencoders. Issue 1 (April 2016)
- Record Type:
- Journal Article
- Title:
- Cross-lingual sentiment classification with stacked autoencoders. Issue 1 (April 2016)
- Main Title:
- Cross-lingual sentiment classification with stacked autoencoders
- Authors:
- Zhou, Guangyou
Zhu, Zhiyuan
He, Tingting
Hu, Xiaohua - Abstract:
- Abstract Cross-lingual sentiment classification is a popular research topic in natural language processing. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and the target language data. In this article, we propose a new model which uses stacked autoencoders to learn language-independent high-level feature representations for the both languages in an unsupervised fashion. The proposed framework aims to force the aligned input bilingual sentences into a common latent space, and the objective function is defined by minimizing the input and output vector representations as well as the distance of the common representations in the latent space. Sentiment classifiers trained on the source language can be adapted to predict sentiment polarity of the target language with the language-independent high-level feature representations. We conduct extensive experiments on English–Chinese sentiment classification tasks of multiple data sets. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.
- Is Part Of:
- Knowledge and information systems. Volume 47:Issue 1(2016:Apr.)
- Journal:
- Knowledge and information systems
- Issue:
- Volume 47:Issue 1(2016:Apr.)
- Issue Display:
- Volume 47, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 47
- Issue:
- 1
- Issue Sort Value:
- 2016-0047-0001-0000
- Page Start:
- 27
- Page End:
- 44
- Publication Date:
- 2016-04
- Subjects:
- Sentiment classification -- Cross-lingual -- Stacked autoencoder
Expert systems (Computer science) -- Periodicals
Information storage and retrieval systems -- Periodicals
006.33 - Journal URLs:
- http://link.springer-ny.com/link/service/journals/10115/index.htm ↗
http://www.springerlink.com/content/0219-1377 ↗
http://www.springer.com/gb/ ↗ - DOI:
- 10.1007/s10115-015-0849-0 ↗
- Languages:
- English
- ISSNs:
- 0219-1377
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5100.437300
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9884.xml