SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings
This work addresses the challenge of improving answer retrieval in online forums for users and developers, but it is incremental as it builds on existing embedding methods for a specific competition task.
The paper tackled the problem of ranking relevant answers in community question answering by using semantic similarity features based on fine-tuned word embeddings and topic similarities, achieving third place in SemEval-2016 Task 3 with a MAP of 51.68 and accuracy of 69.94 in Subtask C, and MAP of 77.58 and accuracy of 73.39 in Subtask A.
We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In Subtask A, our primary submission was also third, with MAP of 77.58 and accuracy of 73.39.