Cross-lingual opinion analysis via negative transfer detection

Lin Gui, Ruifeng Xu, Qin Lu, Jun Xu, Jian Xu, Bin Liu, Xiaolong Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

26 Citations (Scopus)


Transfer learning has been used in opinion analysis to make use of available language resources for other resource scarce languages. However, the cumulative class noise in transfer learning adversely affects performance when more training data is used. In this paper, we propose a novel method in transductive transfer learning to identify noises through the detection of negative transfers. Evaluation on NLP&CC 2013 cross-lingual opinion analysis dataset shows that our approach outperforms the state-of-the-art systems. More significantly, our system shows a monotonic increase trend in performance improvement when more training data are used.
Original languageEnglish
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
ISBN (Print)9781937284732
Publication statusPublished - 1 Jan 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014


Conference52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
Country/TerritoryUnited States
CityBaltimore, MD

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language


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