research-article

Free access

Cross lingual adaptation: an experiment on sentiment classifications

Authors:

Christopher PalAuthors Info & Claims

ACLShort '10: Proceedings of the ACL 2010 Conference Short Papers

Pages 258 - 262

Published: 11 July 2010 Publication History

Abstract

In this paper, we study the problem of using an annotated corpus in English for the same natural language processing task in another language. While various machine translation systems are available, automated translation is still far from perfect. To minimize the noise introduced by translations, we propose to use only key 'reliable" parts from the translations and apply structural correspondence learning (SCL) to find a low dimensional representation shared by the two languages. We perform experiments on an English-Chinese sentiment classification task and compare our results with a previous cotraining approach. To alleviate the problem of data sparseness, we create extra pseudo-examples for SCL by making queries to a search engine. Experiments on real-world on-line review data demonstrate the two techniques can effectively improve the performance compared to previous work.

References

[1]

}}Rie Kubota Ando and Tong Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research.

Digital Library

[2]

}}Carmen Banea, Rada Mihalcea, Janyce Wiebe, and Samer Hassan. 2008. Multilingual subjectivity analysis using machine translation. In Proceedings of EMNLP.

Digital Library

[3]

}}Nuria Bel, Cornelis H. A. Koster, and Marta Villegas. 2003. Cross-lingual text categorization. In Research and AdvancedTechnology for Digital Libraries.

[4]

}}John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspondence learning. In Proceedings of EMNLP.

Digital Library

[5]

}}John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. 2007a. Learning bounds for domain adaptation. In Proceedings of NIPS.

[6]

}}John Blitzer, Mark Dredze, and Fernando Pereira. 2007b. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of ACL.

[7]

}}Danushka Bollegala. 2007. Measuring semantic similarity between words using web search engines. In Proceedings of WWW 07.

Digital Library

[8]

}}Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. 2007. Co-clustering based classification for out-of-domain documents. In Proceedings of KDD.

Digital Library

[9]

}}Jing Jiang and ChengXiang Zhai. 2007. A two-stage approach to domain adaptation for statistical classifiers. In Proceedings of CIKM.

Digital Library

[10]

}}Ryan T. McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeffrey C. Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Proceedings of ACL.

[11]

}}Rada Mihalcea, Carmen Banea, and Janyce Wiebe. 2007. Learning multilingual subjective language via cross-lingual projections. In Proceedings of ACL.

[12]

}}Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of EMNLP.

Digital Library

[13]

}}Peter D. Turney. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL.

Digital Library

[14]

}}Xiaojun Wan. 2008. Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In Proceedings of EMNLP.

Digital Library

[15]

}}Xiaojun Wan. 2009. Co-training for cross-lingual sentiment classification. In Proceedings of ACL.

Digital Library

Cited By

Zhao CWu MYang XZhang WZhang SWang SLi D(2024)A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and ProspectsACM Computing Surveys10.1145/364510656:7(1-37)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3645106
Pirbonyeh ARezaie VParvin HNejatian SMehrabi M(2019)A linear unsupervised transfer learning by preservation of cluster-and-neighborhood data organizationPattern Analysis & Applications10.1007/s10044-018-0753-922:3(1149-1160)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10044-018-0753-9
Mourio Garca MPrez Rodrguez RAnido Rifn L(2017)Wikipedia-based cross-language text classificationInformation Sciences: an International Journal10.1016/j.ins.2017.04.024406:C(12-28)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.04.024
Show More Cited By

Index Terms

Cross lingual adaptation: an experiment on sentiment classifications
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Cross-Lingual Adaptation Using Structural Correspondence Learning

Cross-lingual adaptation is a special case of domain adaptation and refers to the transfer of classification knowledge between two languages. In this article we describe an extension of Structural Correspondence Learning (SCL), a recently proposed ...
Cross-lingual adaptation as a baseline: adapting maximum entropy models to Bulgarian
AdaptLRTtoND '09: Proceedings of the Workshop on Adaptation of Language Resources and Technology to New Domains

We describe our efforts in adapting five basic natural language processing components to Bulgar-ian: sentence splitter, tokenizer, part-of-speech tagger, chunker, and syntactic parser. The components were originally developed for English within OpenNLP, ...
Translation model based cross-lingual language model adaptation: from word models to phrase models
EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

In this paper, we propose a novel translation model (TM) based cross-lingual data selection model for language model (LM) adaptation in statistical machine translation (SMT), from word models to phrase models. Given a source sentence in the translation ...

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

ACLShort '10: Proceedings of the ACL 2010 Conference Short Papers

July 2010

392 pages

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 11 July 2010

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
531
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)4

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhao CWu MYang XZhang WZhang SWang SLi D(2024)A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and ProspectsACM Computing Surveys10.1145/364510656:7(1-37)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3645106
Pirbonyeh ARezaie VParvin HNejatian SMehrabi M(2019)A linear unsupervised transfer learning by preservation of cluster-and-neighborhood data organizationPattern Analysis & Applications10.1007/s10044-018-0753-922:3(1149-1160)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10044-018-0753-9
Mourio Garca MPrez Rodrguez RAnido Rifn L(2017)Wikipedia-based cross-language text classificationInformation Sciences: an International Journal10.1016/j.ins.2017.04.024406:C(12-28)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.04.024
Zhou GZeng ZHuang JHe TPerego RSebastiani FAslam JRuthven IZobel J(2016)Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural NetworksProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911490(245-254)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911490
Heyman GVulić IMoens M(2016)C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared contentData Mining and Knowledge Discovery10.1007/s10618-015-0442-x30:5(1299-1323)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10618-015-0442-x
Argueta CSaravia EChen Y(2015)Unsupervised Graph-Based Patterns Extraction for Emotion ClassificationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2809419(336-341)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2809419
Hajmohammadi MIbrahim RSelamat AFujita H(2015)Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samplesInformation Sciences: an International Journal10.1016/j.ins.2015.04.003317:C(67-77)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.ins.2015.04.003
Xu RGui LXu JLu QWong K(2015)Cross lingual opinion holder extraction based on multi-kernel SVMs and transfer learningWorld Wide Web10.1007/s11280-013-0246-018:2(299-316)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1007/s11280-013-0246-0
Lin ZJin XXu XWang YTan SCheng X(2014)Make It PossibleProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0210.1109/WI-IAT.2014.83(79-86)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.83
Balamurali AKhapra MBhattacharyya P(2013)Lost in translationProceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 210.1007/978-3-642-37256-8_4(38-49)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37256-8_4
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents