Category-Sensitive Question Routing in Community Question Answering

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Presentation transcript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dataset 20/4/2019 Paper Presentation 17/24

Dataset 20/4/2019 Paper Presentation 18/24

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

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

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

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

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

THANKS! 20/4/2019 Paper Presentation