Answer generation method and device based on artificial intelligence, computer equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an answer generation method and device based on artificial intelligence, computer equipment and a medium. The method comprises the steps: acquiring data information, and using an information recognition model corresponding to the information type of the data information to recognize the data information to obtain a text; when the length of the text is greater than a preset length threshold, segmenting the text according to a preset sliding window to obtain a plurality of text segments; receiving the query information and generating a query question corresponding to the query information according to the query information and a preset question template; splicing each text fragment and the query problem toobtain input information, and inputting the input information into a BERT model to obtain a feature vector corresponding to each text character in the input information; and determining a question answer corresponding to the query question in the text fragment according to a preset calculation formula and the feature vector. The question answering accuracy of the question answering system can beimproved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based answer generation method, device, computer equipment and media. Background technique [0002] Question Answering System (Question Answering System, QA) is an advanced form of information retrieval system, which integrates technologies such as information retrieval, information extraction, and natural language processing. question. Usually, the question answering system extracts information from the acquired data to obtain the corresponding answers to the questions. However, due to the flexibility of Chinese expressions, the positions of keywords with the same semantic sentences are not fixed, so it is easy to extract wrong answers. At present, information extraction is usually based on the named entity recognition model. Due to the restriction of the named entity recognition model on the input text, the lack of informati...

AI Technical Summary

Problems solved by technology

Usually, the question answering system extracts information from the acquired data to obtain the corresponding answers to the questions. However, due to the flexibility of Chinese expressions, the positions of keywords with the same semantic sentences are not fixed, so it is easy to extract wrong answers.

At present, information extraction is usually based on the named entity recognition model. Due to the restriction of the named entity recognition model on the input text, the lack of information may be caused, making it difficult to extract complete data information, so it is easy to lead to low accuracy of information extraction, resulting in The question answering system is less accurate in answering questions

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