부산대학교 도서관

Questions which are syntactically different, yet having the same intent, give a poor encounter to both the writer of an answer as well as the individual who searches for the answer in social Q&A online platforms such as Quora, Yahoo Answers, and StackOverflow. In order to maintain a rich and diverse database of answers, ensuring the uniqueness of every question on such Q&A platforms is of utmost necessity. The objective of this proposed work is to eliminate redundancy/duplicacy of userentered questions so as to increase the relevance of the answer(s) provided to semantically similar questions. This problem is a closed challenge taken from Kaggle, an online platform to learn and compete in data science challenges. The dataset worked upon in this paper has been obtained from Kaggle and LSTM with Euclidean Distance outperform other algorithms with log loss of 0.14.