This paper describes the system we submitted to the task 3 (Community Question Answering) in SemEval 2016, which contains three subtasks, i.e., Question-Comment Similarity (subtask A), Question-Question Similarity (subtask B), and Question-External Comment Similarity (subtask C). For subtask A, we employed three different methods to rank question-comment pair, i.e., supervised model using traditional features, Convolutional Neural Network and Long-Short Term Memory Network. For subtask B, we proposed two novel methods to improve semantic similarity estimation between question-question pair by integrating the rank information of questioncomment pair. For subtask C, we implemented a two-step strategy to select out the similar questions and filter the unrelated comments with respect to the original question.
REF for instance used different ranking methods such as supervised models using traditional features as well as convolutional neural network and long-short term memory.
ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering
SemEval@NAACL-HLT
Computer Science