Answer searching method and device based on semantic index and related equipment thereof

Abstract

The invention discloses an answer search method and device based on semantic indexing, electronic equipment and a storage medium. The method comprises the steps of obtaining a question text input by auser; carrying out vector conversion on the problem text according to a pre-trained semantic index model to obtain semantic vector expression of the problem; matching the semantic vector expression of the question with each answer vector expression in a pre-established answer vector index library; wherein the answer vector index library is constructed by converting all answers in an answer samplepool into vector expressions according to a semantic index model; and obtaining a corresponding answer text according to the matched answer vector expression, and providing the corresponding answer text as a search result for the user. The method can solve the technical problem that in the prior art, a common similarity matching technology only simply matches questions from a character surface, so that only answers related to some character surfaces can be obtained, the answer screening time of a user is saved, and the use experience of the user is improved.

Description

technical field [0001] The present invention relates to the field of search technology, in particular to a semantic index-based answer search method, device, electronic equipment and computer-readable storage medium. Background technique [0002] Questions that require answers are common on the Internet, such as "what is artificial intelligence", "how does influenza spread", "the author of 101 realizations of water", etc. Currently, users can only obtain relevant information through search engines. In related technologies, when a search engine receives a question text input by a user, it uses similarity matching technology to find an answer from a large amount of data, such as calculating the similarity between the question and the answer, and selecting the best match as the answer to the question. [0003] However, the current problem is that the above-mentioned traditional similarity matching technology can only model symmetric matching, and its partial matching is literal...

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Problems solved by technology

[0003] However, the current problem is that the above-mentioned traditional similarity matching technology can only model symmetric matching, and its partial matching is literal. For example, the question is "Why is the sky blue?" Using the traditional similarity matching technology, Answers that match with high similarity are often sentences related to "sky" and "blue" literally. They are not real answers, but users need to choose from these answers that may be their real answers, wasting users' time , and poor user experience

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