1. A Classification-based Approach to Question Routing in Community Question Answering
Tom Chao Zhou
22, Feb, 2010
Department of Computer Science and Engineering
The Chinese University of Hong Kong

2. Outline Background Motivation Related Work Proposed Approach
Experiments
Conclusions and future work

3. Background Community-based Question and Answering service (CQA):
Yahoo! Answers, Answers.com (English)
Naver’s Knowledge In (Korean)
Baidu Knows (Baidu), Tianya Wenda (Google), Soso Wenwen (Tencent) (Chinese)
Ask and answer natural language questions.
Google, Baidu, Bing: keyword search
Direct answer.
Browse Web pages.
Efficient.
Natural Language Question Answering System
Leverage community wisdom.

4. Background Statistics of Yahoo! Answers
More than 1 billion questions and answers posted.
The Answers community counts more than 179 million users.
26 markets and 12 languages.
Everyday, 15 million users visit the site.

5. Background
Statistics of Baidu Knows
Solved questions: 76,185,152

6. Background
Concern: whether users can get their posted questions resolved in a reasonable period.

7. Background Asker’s perspective:
Randomly sample 3,640 questions from Yahoo! Answers.
After one day, 434 (11.95%) of questions get resolved.
726 (19.95%) questions get resolved in two days.
A large number of posted questions cannot get resolved in a reasonable period.

8. Background Answerer’s perspective:
Whether users are willing to answer questions?
40, 000 users from Yahoo! Answers.
Analyze the relationship between the number of questions a user has answered and the number of questions the user has asked.
Participants in a CQA service are willing to contribute their knowledge.

9. Motivation Problem Existed: A lot of question cannot get resolved.
Users are willing to provide answers.
Overwhelmed by the large number of open questions.
Cannot find interesting questions.

10. Motivation Question Routing: Asker: Answerer: System:
Route questions to suitable answerers.
Asker:
Reduce the time lag between the time a question is posted and the time it is answered.
Answerer:
Receive questions he/she is interested in instead of a large number of unfiltered questions, the answerer would become more enthusiastic in providing answers.
System:
Linking open questions with suitable answerers, fully leverage users’ answering passion.

11. Motivation Problem Definition: Question Routing Problem:
Consider the problem as a classification task.
Inspired by Agichtein et al., Liu et al., Hong et al..
Question Routing Problem:
Given a question and a user in CQA, determine whether the user will contribute his/her knowledge to answer the question.

12. Related Work Finding high quality content.
Link analysis and expert finding.
Question search, suggestion and recommendation.

13. Proposed Approach A question + A user: A classification task.
Local features.
Global features.

14. Proposed Approach Local features: Question feature.
User History Feature.
Question-user relationship features.

15. Local Features

16. Proposed Approach Global features:
Take into account the global information of the CQA service.
Typically a question belongs to a certain category, and questions in the same category would discuss similar topics.
Smoothing effect.

18. Experiments Classification algorithm: Metrics:
SVM: libsvm with linear kernel.
Metrics:
Precision, Recall, F1 and Accuracy.

19. Experiments Datasets: Yahoo! Answers.
3,500 users. 1,325,225 questions answered and 88,852 questions asked.
Positive instance (question-user pair): if a user answered a question.
Negative instance: if a user asked a question.

20. Experiments
Effect of local features

21. Experiments
Top-10 local
features.

22. Experiments
Effect of Local and Global Features

23. Experiments
Top-10 local
and global features.

24. Experiments
Consistency Analysis

25. Conclusions and future work
Question routing problem.
Classification approach.
Feature analysis.
Future Work:
One-class SVM.
Semi-supervised approach.
Probabilistic aspect.

26. Thanks!
Q & A