ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository

Proceedings of SemEval-2016, pages 904–909, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics ITNLP-AiKF at SemEval-2016 Task 3: a question answering system using community QA repository Chang’e Jia1, Xinkai Du2, Chengjie Sun1 and Lei Lin1 1Harbin Institute of Technology, Harbin Heilongjiang 150001, China 2Beijing Huilan Technology Co.,Ltd. {cejia, cjsun, linl}@insun.hit.edu.cn duxk@huilan.com Abstract Community Question Answering (CAQ) sys- tems play an important role in people's lives due to the huge knowledge accumulated in them. In order to take full advantage of the huge knowledge, the target of semeval2016 task3 is to find the best answers to a new question in CQA. This work proposes to use rich semantic text similarity (STS) features to complete the task. We address the task as a ranking problem and Support Vector Regres- sion (SVR) model is chosen to combine rich semantic similarity features and context fea- tures. Finally, we used genetic algorithm to do feature selection. Our method achieves an MAP (mean average precision) of 71.52%, 71.43% and 48.49% in subtask A, B and C respectively. It ranked 8th in subtask A and subtask B, and 7th in subtask C. 1 Introduction The CQA system with interactive and open character, can better adapt to the diversity of needs of users. With the growth of the number of users, community question answering system has accumulated a lot of QA pair archives. It has presented new challenges to analyze user's requirement and recommends high-quality answers to users. In response to this problem, Semeval2015 Task3 - “Answer Selection in Community Question Answering”1 (Nakov et al., 2015) proposed a task to divide the answers into three 1 http://alt.qcri.org/semeval2015/task3/ levels in accordance with the relevance of the question. However, the classification system does not fully comply with the question requirement, as it does not implement the recommending function. Semeval2016 Task3 - “Community Question Answering”2 (Nakov et al., 2016) puts forward new requirements to automate the process of finding good answers to new questions in a community-created discussion forum based on the Semeval2015 Task3. The task is divided into three parts: subtask A - “Question-Comment Similarity”, subtask B - “Question-Question Similarity” and subtask C - “Question-External Comment Similarity”. In our work, we focus on using features that employ STS knowledge, such as extracting text similarity features from word vectors, structured resource and topic models, to deal with the task. Word vectors has been used in (Liu, Sun, Lin, Zhao, & Wang, 2015) and (Nicosia et al., 2015) to compute STS, and (Jin, Sun, Lin, & Wang, 2014) has evaluated word-phrase semantic similarity with structured resource. 2 Feature The main idea of our method is to find the similarity between most similar words in two sentences to estimate sentence similarity. Our features include the following categories: WordNet-based features, vector features, word matching features, topic features and answer features. 2 http://alt.qcri.org/semeval2016/task3/ 904 2.1 Vector Features There are three approaches that we are applying to measure sentence similarity with word vector. The first one uses the sum of all the words’ …

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