کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
533804 | 870167 | 2015 | 6 صفحه PDF | دانلود رایگان |
• We improve the co-training method to predict the quality of answers.
• Two semi-supervised learning methods based on co-training are presented.
• Surface linguistic features and social features are proposed.
Predicting the quality of user-generated answers is definitely of great importance for community-based question answering (CQA) due to the frequent occurrence of low-quality answers. Most existing answer quality prediction works combine non-textual features of user-generated answers directly without considering the diversity of non-textual features. In this paper, we propose two co-training approaches: random subspace split-based co-training (RSS-CoT) and content and social split-based co-training (CS-CoT) to predict the quality of answers by mining the relationships of non-textual features and unlabeled data in CQA. Our results demonstrate that both appropriate combination of non-textual features and unlabeled data can promote the prediction performance of answer quality.
Journal: Pattern Recognition Letters - Volume 58, 1 June 2015, Pages 29–34