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Category-Sensitive Question Routing in Community Question Answering

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Presentation on theme: "Category-Sensitive Question Routing in Community Question Answering"— Presentation transcript:

1 Category-Sensitive Question Routing in Community Question Answering
Prepared and Presented by Baichuan Li

2 Outline Introduction & Motivation Category Sensitive QR Experiments
Conclusion 20/4/2019 Paper Presentation 2/24

3 Introduction Community Question-Answering (CQA) Services 3/24
20/4/2019 Paper Presentation 3/24

4 Low participation rate
Motivation Low participation rate 20/4/2019 Paper Presentation 4/24

5 Motivation States of tracked question in Yahoo! Answers
and Baidu Zhidao within 72 hours Long lag time 20/4/2019 Paper Presentation 5/24

6 Question Routing Definition Framework
Routing a new posted question to users who are most likely to give answers. Framework 20/4/2019 Paper Presentation 6/24

7 Related work Previous Methods Limitations
LMs (Liu et al. 2005, Zhou et al. 2009, Li and King 2010) PLSA (Qu et al. 2009) LDA + LM (Liu et al. 2010) Limitations Based on all previous answered questions Many questions are irrelevant to the routed question 20/4/2019 Paper Presentation 7/24

8 Category Information Why help? Lower cost Higher precision 8/24
20/4/2019 Paper Presentation 8/24

9 Category-Sensitive QR
Query likelihood language model BCS-QL TCS-QL Translation-based language model BCS-TB TCS-TB 20/4/2019 Paper Presentation 9/24

10 Query likelihood LM 20/4/2019 Paper Presentation 10/24

11 BCS-QL 20/4/2019 Paper Presentation 11/24

12 TCS-QL 20/4/2019 Paper Presentation 12/24

13 TCS-QL 20/4/2019 Paper Presentation 13/24

14 Translation-based LM 20/4/2019 Paper Presentation 14/24

15 BCS-TB & TCS-TB 20/4/2019 Paper Presentation 15/24

16 Methods Compared LDALM CBLM 20/4/2019 Paper Presentation 16/24

17 Dataset 20/4/2019 Paper Presentation 17/24

18 Dataset 20/4/2019 Paper Presentation 18/24

19 Evaluation Metrics SR(K) MRR Mean QR Time
the average of time spent on routing one question 20/4/2019 Paper Presentation 19/24

20 Experimental Results 20/4/2019 Paper Presentation 20/24

21 Experimental Results 20/4/2019 Paper Presentation 21/24

22 Experimental Results 20/4/2019 Paper Presentation 22/24

23 Conclusion Proposed basic and transferred category- sensitive models
Integrated the models with QLLM and TBLM Studied various methods’ performance comparing with previous approaches Future work Better user modeling Personalized top K selection 20/4/2019 Paper Presentation 23/24

24 THANKS! 20/4/2019 Paper Presentation

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