Question answering method, question answering device, computer equipment and storage medium

Abstract

The invention discloses a question answering method and device, computer equipment and a storage medium. The question answering method comprises the steps: obtaining input information of a user; recognizing named entities in the input information, and linking the named entities to candidate entities, corresponding to the named entities, in the Chinese knowledge graph, to form entity pairs, whereinthe entity pairs comprise the named entities and the candidate entities; matching candidate relations of candidate entities in the Chinese knowledge graph through a relation model; according to the entity pairs and the candidate relations, forming candidate triples, wherein the candidate triples comprise named entities, candidate entities and candidate relations; obtaining a sorting result corresponding to each candidate triple based on the learning sorting model; and querying the Chinese knowledge graph according to a sorting result to obtain an answer to the input information. According tothe question answering method, external resources can be effectively utilized, and a large amount of context information can be provided through text mining, and based on the learning sorting model, good answers can be obtained when question and answer corpus data are few.

Description

technical field [0001] The invention relates to the technical field of computers, in particular to a question answering method, a question answering device, computer equipment and a storage medium. Background technique [0002] Question answering system is an advanced form of information retrieval system, which can answer questions raised by users in natural language with accurate and concise natural language. The traditional question answering system is divided into two parts: question processing and answer retrieval. Among them, the basis of question processing is word segmentation. Answer retrieval mostly uses a scoring mechanism, that is, a series of candidate answers are selected from massive text data, and then a selection function is constructed to select the closest answer from the candidate answers. However, this traditional question-answering device has different degrees of errors due to the difference in processing long text nouns and the selection function cons...

AI Technical Summary

Problems solved by technology

[0004] The rule-based knowledge base question answering system is highly accurate, but not flexible. Each type of question needs to write a rule, while template learning and deep learning methods often need to learn from a large-scale question answering corpus, which is difficult to apply in the early stage of development. In a vertical field where question answering data is scarce

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