Question similarity computing method for automatic question answering system
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Abstract:
Structured automatic question answering system lacks the division of vocabulary, word order and structure, which leads to low sentence similarity. In order to solve this problem, a method of computing sentence similarity based on Web semantics is proposed. According to the structure of the structured automatic question answering system, the system statement analysis model is designed. Through the forward matching method, the user input natural statements in the model professional lexicon are segmented and the string relationship is analyzed. The unsteady similarity coefficient is used to describe the similarity of two strings, and the similarity of morphology, word order and structure is analyzed to determine the similarity of different sentences. The experimental results show that with this method, the minimum computational accuracy is 42%, and the maximum computational accuracy can reach 96%. The overall computational accuracy has been greatly improved compared with that based on neural network and ant colony, thereby improving the overall performance of the automatic question answering system.
