Entity recognition method and device and computer equipment

Abstract

The invention provides an entity recognition method and device and computer equipment. The method comprises the steps of performing recall processing on to-be-identified corpora; adopting a dictionarymatching module to recall possible candidate slot position combinations of the corpus to be identified; marking the to-be-identified corpus according to the to-be-identified corpus; acquiring a corresponding slot position labeling sequence: respectively obtaining a coding vector of a corpus to be identified and a coding vector of each slot marking sequence by utilizing a deep learning network, selecting the coding vector of the slot marking sequence most similar to the coding vector of the corpus to be identified, and taking the slot marking sequence corresponding to the coding vector as an optimal slot marking sequence, thereby obtaining a named entity of the corpus to be identified. Therefore, by utilizing the advantages of the rule template and the deep learning algorithm, the named entities of various types of corpora can be quickly, simply and accurately identified, the cold start problem is solved, the obtained identification result is generally a computer language, and the electronic equipment can directly respond to the identification result.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to an entity recognition method, device and computer equipment. Background technique [0002] In recent years, with the development of artificial intelligence, human-computer dialogue systems have been widely applied to application platforms in various fields. The man-machine dialogue system is a computer system that can communicate with people. After obtaining the questions raised by the users, it needs to identify the named entities in the questions, so as to give the answers or corresponding operations required by the users, which simplifies the human-computer dialogue system. The flow of interaction. [0003] At present, in the application of named entity recognition (Named Entity Recognition, NER), it is proposed to use deep learning to identify named entities in the corpus, that is, to label named entities as sequences, and use large-scale corpus to ...

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Problems solved by technology

However, this entity recognition method requires a large amount of high-quality labeled corpus for model training, and the feasibility is poor; it can only recognize some simple entities. For proper nouns and ambiguous nouns, the recognition accuracy is low, and the update frequency is high. entity that doesn't even recognize

[0004] In this regard, the technicians proposed a method of using rule templates to identify named entities in the corpus, that is, to match the pre-constructed templates with the target corpus, and identify the named entities in the target corpus. Although this method can accurately identify all complex However, in the case of complex product functional domains, it is necessary to construct a large number of templates, the workload is huge, and the post-maintenance and transferability are poor, and it is difficult to reuse them in different scenarios

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