Named entity identification method, device and equipment and computer readable storage medium

Abstract

The embodiment of the invention discloses a named entity identification method, device and equipment and a computer readable storage medium. The method comprises the steps that character vectors and word vectors of a to-be-identified text are acquired, and weighted sum is carried out on the character vectors and the word vectors to obtain a weighted sum result; the weighted sum result is input into a target two-way LSTM model for processing to obtain a text character sequence; and the text character sequence is input into a target CRF model for processing to obtain a named entity identification result of the to-be-identified text. After the character vectors and word vectors of the to-be-identified text are acquired, weighted sum is carried out on the character vectors and the word vectors, dynamic weight information is used better, a relationship among words of a context is fully considered by adopting a two-way LSTM model, two-way information is fully adopted, then processing is carried out combined with a CRF model, thereby improving the accuracy rate of the named entity identification.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of the Internet, and in particular to a named entity recognition method, device, equipment, and computer-readable storage medium. Background technique [0002] In the scenarios of natural language processing tasks such as information extraction and entity linking, NER (NamedEntity Recognition, named entity recognition) is often required. Among them, NER refers to the process of identifying a specific type of thing name or symbol in a document collection. [0003] In related technologies, when performing named entity recognition, models such as CRF (Conditional Random Field, conditional random field algorithm) or unidirectional RNN (Recurrent neural Network, cyclic neural network) are generally used to recognize the text to be recognized. [0004] However, no matter whether CRF or unidirectional RNN is used for recognition, the semantic information obtained is relatively limited, so...

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Problems solved by technology

[0004] However, since the semantic information obtained is relatively limited regardless of whether CRF or unidirectional RNN is used for recognition, the accuracy of recognition is not high.

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