A Multi-Dimensional Model for Assessing the Quality of Answers in Social Q&A Sites
作者： Iryna Gurevych , Delphine Bernhard , Zhemin Zhu
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摘要: The quality of user-generated content in the Web 2.0 dramatically varies from professional to abusive. Quality assessment is therefore a critical problem producing, managing and retrieving contents 2.0. In this paper, we consider task assessing content, represented natural language, as crossdisciplinary application covered both by IQ (Information Quality) NLP (Natural Language Processing). We develop multi-dimensional model for answers social Q&A (Question & Answer) sites context eLearning. Based on model, further connect potential techniques with these dimensions automate assessment. Preliminary results show that it feasible which opens wide arena areas NLP.
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