Will My Question Be Answered? Predicting "Question Answerability" in Community Question-Answering Sites
Abstract
All askers who post questions in Community-based Question Answering (CQA) sites such as Yahoo! Answers, Quora or Baidu’s Zhidao, expect to receive an answer, and are frustrated when their questions remain unanswered. We propose to provide a type of “heads up” to askers by predicting how many answers, if at all, they will get. Giving a preemptive warning to the asker at posting time should reduce the frustration effect and hopefully allow askers to rephrase their questions if needed. To the best of our knowledge, this is the first attempt to predict the actual number of answers, in addition to predicting whether the question will be answered or not. To this effect, we introduce a new prediction model, specifically tailored to hierarchically structured CQA sites. We conducted extensive experiments on a large corpus comprising 1 year of answering activity on Yahoo! Answers, as opposed to a single day in previous studies. These experiments show that the F 1 we achieved is 24% better than in previous work, mostly due the structure built into the novel model.
Cite
Text
Dror et al. "Will My Question Be Answered? Predicting "Question Answerability" in Community Question-Answering Sites." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_32Markdown
[Dror et al. "Will My Question Be Answered? Predicting "Question Answerability" in Community Question-Answering Sites." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/dror2013ecmlpkdd-my/) doi:10.1007/978-3-642-40994-3_32BibTeX
@inproceedings{dror2013ecmlpkdd-my,
title = {{Will My Question Be Answered? Predicting "Question Answerability" in Community Question-Answering Sites}},
author = {Dror, Gideon and Maarek, Yoelle and Szpektor, Idan},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {499-514},
doi = {10.1007/978-3-642-40994-3_32},
url = {https://mlanthology.org/ecmlpkdd/2013/dror2013ecmlpkdd-my/}
}