Research on Industry Question Answering System Based on Near Neighbor Vector Search

Le résumé fourni par la source

Abstract In the field of natural language processing, language-based question answering has been widely studied and made great achievements. As an important form of search engine, question answering system can further improve the search quality compared with traditional search engines. Traditional tree-based indexing methods are inefficient in high-dimensional space. This paper focuses on the application of k nearest neighbor search in large-scale image databases, and researches on high-dimensional indexing technology based on vector approximation method. Fast-Hessian detector is used to detect feature points and generate SURF feature description vector; Then, the initial matching point pair is obtained by fast approximate nearest neighbor search algorithm, and then the unidirectional matching results are matched bidirectionally; at the same time, it makes a detailed analysis of the situation that the events in the sentences are verbs of “abstract change”, so as to realize the automatic question and answer after the change of abstract relations between entities.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.