A PDD Approach for Expert Finding

1. A PDD Approach for Expert Finding Fu Yupeng Tsinghua Univ

2. Backgroud • Finding a expert in an organization using corporate information • Frequently asked question • Not well addressed in research • Hot research topic in recent years

3. Characteristics • Build up a relationship between topics and experts via documents • Deal with a mixture of types and formats information in corporate • Integrate different kinds of expertise data • Models for matching variants of person names and name disambiguation • Expertise information recognition and extraction • QA techniques

4. TREC2005 Expert Finding Task • Aim: Given a query about a region, return a expert list on that domain • Resource: • Full W3C corpus • 1092 candidate list, each one with a unique personid • 10 training queries and 50 test queries

5. Resource distribution

6. Results

7. Expertise search model • How deal with topic and expert via documents？ • Query-time generated model • Aggregate model Collection Extract Topics Relative Docs Person Profiles Extract Topics Experts Experts

8. Construction of PDD • Person Description Document (PDD) Context information Distance weighted functional information Group information

9. Experiments • Component of PDD Performance with different features used solely as PDD

10. Experiments • Effect of word pair based ranking model

11. Experiments • Effect of word pair based ranking model Comparison of wordpair-based ranking model effect on features of PDD

12. Experiments Comparative best results between PDD-based search model and our contrastive model

13. THU vs MSRA • Context Window • Features • Title,heading12,bold,anchor text • Title, all heading info • People clustering • Structure-based extraction VS Context Vector

14. Future Work • Employ other resources, especially Emails • Other applications • Software search • MP3 search

15. Example

16. Thanks！