Intelligent Question and Answering System Based on SVM and Cosine Similarity

Wan-li SONG, Wei-wei CHEN, Ming-zhu ZHANG

Abstract


It is a tough task for teachers to answer all questions from students effectively and timely. In this paper, we design and implements an intelligent question answering system using Natural Language Processing, template classification, support vector machine. This system also calculates the similarity between the question and answer pairs by cosine similarity algorithm, and returns the most similar answer. If the user is not satisfied with the answer, the system will write the question into the public section to fall back on other users. The answer will be evaluated and added to the QA base if it is passed with the corresponding question. So that the questions and answers in the QA base continue to expand. We use the QA base of a network forum as the basic library to carry out the experiments. The implementation and experimental results indicate that the proposed approach is achievable.

Keywords


Intelligent system, Question classification, Question similarity, Cosine similarity


DOI
10.12783/dtcse/mmsta2017/19714

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