Sequence labeling method and device in natural language processing, equipment and storage medium

A technology of natural language processing and sequence labeling, which is applied to instruments, multimedia data retrieval, computing, etc., can solve the problems of poor labeling effect, affecting labeling effect, and poor universality of latent variable conditional random field models, so as to improve the effect and improve universal effect

Pending Publication Date: 2019-06-14
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL +1
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Problems solved by technology

[0004] However, when the text sequence is tagged by the hidden variable conditional random field model, different coding modes have different tagging effects on different text sequences. For example, a certain coding mode has a better tagging effect on text sequence 1, However, the labeling effect on the text sequence 2 may be poor, resulting in poor universality of the latent variable conditional random field model and affecting the labeling effect

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  • Sequence labeling method and device in natural language processing, equipment and storage medium
  • Sequence labeling method and device in natural language processing, equipment and storage medium
  • Sequence labeling method and device in natural language processing, equipment and storage medium

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[0059] The exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. Rather, they are merely examples of devices and methods consistent with some aspects of the application as detailed in the appended claims.

[0060] Natural language processing technology is a general term for a class of technical methods used to process speech and text data. Among them, sequence labeling is an important part of natural language processing technology, and the effect of sequence labeling will directly affect the accuracy of natural language processing. This application proposes a sequence labeling method, w...

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Abstract

The invention relates to a sequence labeling method in natural language processing, and the method comprises the steps: obtaining a text sequence, inputting the text sequence into a sequence labelingmodel, obtaining a target path, enabling each node in the target path to be a label in a preset label set, and enabling the preset label set to comprise each label corresponding to m coding modes; andarranging the nodes in the target path according to a sequence from first to second in the target path to obtain a labeling sequence corresponding to the text sequence. According to the invention, atext sequence sample coding result is trained according to a plurality of coding modes to obtain a sequence labeling model; the sequence model label is used for inputting the text sequence, the inputtext sequence is processed through the sequence model label, the annotation sequence corresponding to the text sequence is output, annotation of the text sequence is not limited to a single coding mode, and therefore universality of the sequence annotation model to different inputs is improved, and the effect of sequence annotation is improved.

Description

Technical field [0001] This application relates to the technical field of natural language processing, and in particular to a method, device, equipment and storage medium for sequence labeling in natural language processing. Background technique [0002] Sequence annotation is one of the basic problems often encountered when solving natural language processing problems. The latent variable conditional random field model is a commonly used model for sequence labeling. [0003] In related technologies, the latent variable conditional random field model uses a certain coding mode as the latent variable. When the input text sequence is sequenced, the hidden variable conditional random field model assigns each label of the corresponding coding mode to the input Each text element in the text sequence realizes the labeling of the text elements in the text sequence. [0004] However, when using the latent variable conditional random field model to label text sequences, different coding mod...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/43G06K9/62
Inventor 林浚玮邵轶男陈伟
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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