Matching Question and Answer Using Similarity: An Experiment with Stack Overflow
Abstract
Community question and answer (CQA) sites mostly involve knowledge-bases feeding into their automated question answering systems. This paper focuses on Stack Overflow which is an online community for developers to share knowledge in computer programming. The proposed framework consists of composing of a paired Q&A corpus, followed by building of a document model with the use of paragraph vector in distributed representation via the doc2vec method, then similarity ranking to fetch a matched answer to a given question. The model pairs the two so as to represent the semantic relevance between the questions and answers generated by the proposed method. The initial experimental results have shown the system is able to provide answers automatically and with a performance of 50% accuracy when compared to expert opinions.
Authors: Prissadang Suta, Pornchai Mongkolnam, Chun Che Fung, Jonathan H. Chan
Published in: IEEE International WIE Conference on Electrical and Computer Engineering (2018)