SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives.

Sentiment ambiguous adjectives, which have been neglected by most previous researches, pose a challenging task in sentiment analysis. We present an evaluation task at SemEval-2010, designed to provide a framework for comparing different approaches on this problem. The task focuses on 14 Chinese sent...

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Published in:Language Resources & Evaluation Vol. 47; no. 3; pp. 743 - 756
Main Authors: Wu, Yunfang, Jin, Peng
Format: Article
Published: Springer Nature Sep2013
Subjects:
Online Access:View this record in EBSCOhost
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        atl: SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives.
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        au:
          Wu, Yunfang
          Jin, Peng
        affil:
          Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, Beijing China
          Laboratory of Intelligent Information Processing and Application, Leshan Normal University, Leshan China
      su:
        Adjectives
        Sentiment analysis
        Computational linguistics
        Multilingual computing
        Cross-language information retrieval
        Language & languages
      sug:
        subj:
          Adjectives
          Sentiment analysis
          Computational linguistics
          Multilingual computing
          Cross-language information retrieval
          Language & languages
      keyword:
        SemEval
        Sentiment ambiguous adjectives
        Word sense disambiguation
      ab: Sentiment ambiguous adjectives, which have been neglected by most previous researches, pose a challenging task in sentiment analysis. We present an evaluation task at SemEval-2010, designed to provide a framework for comparing different approaches on this problem. The task focuses on 14 Chinese sentiment ambiguous adjectives, and provides manually labeled test data. There are 8 teams submitting 16 systems in this task. In this paper, we define the task, describe the data creation, list the participating systems, and discuss different approaches.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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