Learning to Rank Questions for Community Question Answering with Ranking SVM

2016/08/15 by Minh-Tien Nguyen, Viet-Anh Phan, Nguyen, Minh-Tien +5
Computer Science · #14J30 (Primary) #Expert finding and Q&A systems #FOS: Computer and information sciences #H.3 #H.3.3 #I.2.7 #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1608.04185

openalex publication_date 2016/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents our method to retrieve relevant queries given a new question in the context of Discovery Challenge: Learning to Re-Ranking Questions for Community Question Answering competition. In order to do that, a set of learning to rank methods was investigated to select an appropriate method. The selected method was optimized on training data by using a search strategy. After optimizing, the method was applied to development and test set. Results from the competition indicate that the performance of our method outperforms almost participants and show that Ranking SVM is efficient for retrieving relevant queries in community question answering.

Citations

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

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