Name
Title | ||
|---|---|---|
Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning. |
Abstract | ||
|---|---|---|
Community Question Answering (CQA) websites provide a platform to ask questions and share their knowledge. Good questions in CQA websites can improve user experiences and attract more users. To the best of our knowledge, a few researches have been studied on the question quality, especially the quality of newly proposed questions. In this work, we consider that a good question is popular and answerable in CQA websites. The community features of questions are extracted automatically and utilized to acquire massive good questions. The text features and asker features of good questions are utilized to train our weakly supervised model based on Convolutional Neural Network to recognize good newly proposed questions. We conduct extensive experiments on the publicly available dataset from StackExchange and our best result achieves F1-score at 91.5%, outperforming the baselines. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69179-4_46 | ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017 |
Keywords | Field | DocType |
Question quality,Weakly supervised learning,CQA,CNN | Data mining,Question answering,Ask price,Convolutional neural network,Computer science,Supervised learning,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
10604 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 9 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanhao Zheng | 1 | 1 | 0.36 |
| Bifan Wei | 2 | 38 | 6.39 |
| Jun Liu | 3 | 3 | 1.06 |
| Meng Wang | 4 | 208 | 8.38 |
| weitong chen | 5 | 155 | 13.19 |
| Bei Wu | 6 | 11 | 4.27 |
| Yihe Chen | 7 | 3 | 2.06 |