Model adversarial training method and device and named entity recognition method and device

A technology of named entities and models, applied in neural learning methods, biological neural network models, instruments, etc.

Active Publication Date: 2020-08-11
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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This sparsity of training data br...

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  • Model adversarial training method and device and named entity recognition method and device
  • Model adversarial training method and device and named entity recognition method and device
  • Model adversarial training method and device and named entity recognition method and device

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Embodiment Construction

[0076] The solutions provided in this specification will be described below in conjunction with the accompanying drawings.

[0077] figure 1 A schematic diagram of an implementation scenario of an embodiment disclosed in this specification. Among them, the sequence of word segmentation that will contain multiple word segmentation Input the feature extraction network, and the feature extraction network can output the feature hidden vector of each word segment , based on each feature latent vector, the distribution probability of each participle in each category can be determined, and based on these distribution probabilities, the classification result of each participle is obtained, that is, the label of which category each participle corresponds to. Categories can be represented by labels. SOS is the start symbol of the word segmentation sequence, and EOS is the end symbol of the word segmentation sequence.

[0078] Named entities (Entity), also known as entity words, ha...

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Abstract

The embodiment of the invention provides a model adversarial training method and device and a named entity recognition method and device. The adversarial training method comprises steps of during training, replacing a first named entity in the first sample sequence with a corresponding original label character to obtain a second sample sequence, determining a first text fragment containing the replaced original label character from the second sample sequence, and determining a classification label value of the first text fragment as a first value for representing a replaced named entity; determining feature implicit vectors of a plurality of segmented words in the second sample sequence by adopting a feature extraction network; determining a first segment vector of the first text segment based on the feature implicit vectors of the plurality of segmented words in the second sample sequence, and inputting the first segment vector into a first discriminator to obtain a first prediction value; determining a first loss value based on a difference between the first prediction value and the first value; updating the first discriminator by taking minimization of the first loss value as atarget; and updating the feature extraction network by taking maximization of the first loss value as a target.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of natural language processing, and in particular, relate to model confrontation training and named entity recognition methods and devices. Background technique [0002] In the field of natural language processing technology, the classification of named entities (Entity) in text sequences is an important direction of research. Named entities have the nature of nouns in the part of speech, including person names, organization names, place names, and all other entity categories identified by names. Broader named entities also include categories such as numbers, dates, currencies, addresses, and more. Accurate recognition of the categories of named entities can improve the accuracy and effectiveness of natural language processing. [0003] Usually, a training set is used to train a model for recognizing named entities, and after the model is trained, a test set is used to test t...

Claims

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Application Information

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IPC IPC(8): G06F40/284G06F40/295G06N3/04G06N3/08
CPCG06F40/284G06F40/295G06N3/049G06N3/084G06N3/044G06N3/045
Inventor 李扬名李小龙姚开盛
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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