Learning to Rank Questions for Community Question Answering with Ranking SVM
This work addresses query retrieval for community question answering, but it is incremental as it applies an existing method to a specific competition dataset.
The paper tackled the problem of retrieving relevant queries for new questions in community question answering by investigating learning to rank methods, with results showing that their optimized Ranking SVM method outperformed most participants in a competition.
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.