CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge

نویسندگان

  • Carmen Banea
  • Yoonjung Choi
  • Lingjia Deng
  • Samer Hassan
  • Michael Mohler
  • Bishan Yang
  • Claire Cardie
  • Rada Mihalcea
  • Janyce Wiebe
چکیده

This article provides a detailed overview of the CPN text-to-text similarity system that we participated with in the Semantic Textual Similarity task evaluations hosted at *SEM 2013. In addition to more traditional components, such as knowledge-based and corpus-based metrics leveraged in a machine learning framework, we also use opinion analysis features to achieve a stronger semantic representation of textual units. While the evaluation datasets are not designed to test the similarity of opinions, as a component of textual similarity, nonetheless, our system variations ranked number 38, 39 and 45 among the 88 participating systems.

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تاریخ انتشار 2013