Research And Implementation Of Python Knowledge Automatic Question Answering System

As a new generation of information retrieval system,the Question Answering System(QAS)is combined natural language processing and artificial intelligence.It allows users to ask questions with natural language sentences,and the system return accurate and concise search answers to users.Combining the automatic question and answer system with the education field has undoubtedly become a hot research direction in the field of "Internet + education".This paper selects "Python program knowledge" as a specific field,and uses the three core parts including question preprocessing,information retrieval and answer extraction of the automatic question answering system as the research entry point,and builds a domain knowledge base through a combination of web crawlers and artificial techniques.Designed and implemented a question answering system in the education field that automatically answers "Python program knowledge".The main contents of this paper are:(1)Analyze the composition of domain data,and build a domain knowledge base for multiple data sources.According to the characteristics of data in the field of programming language,the web crawler technology was used to crawl the domain data from Baidu Encyclopedia and Baidu,respectively,and combined with the knowledge and text of the major colleges and universities to manually build a knowledge base(FAQ)of Python programming field.(2)Research the key technology based on Word2 Vec.Firstly,the traditional TextRank algorithm is used to extract the text feature words and ignore the connection relationship between vocabularies.word2 Vec is used to train the problem corpus text in the knowledge base into a candidate feature word vector set,and based on the similarity and feature words are extracted in a way that there are adjacent relationships to non-uniformly assign node weights.Then Word2Vec's CBOW model is used to train the user's question feature words,and the cosine similarity algorithm is used to calculate the correlation with the question corpus feature word set to achieve question matching.(3)With the technology of Jieba word segmentation and custom computer professional domain dictionary,the accuracy of word segmentation is improved.(4)Use Python language and MySQL database development system,and the technology of Flask web framework,nodejs + vue front-end are achieved system visualization.Designed and implemented a Python knowledge automatic question answering system dedicated to improving students' autonomous learning ability.