Method and system for identifying named entity

A named entity recognition and entity technology, which is applied in special data processing applications, instruments, biological neural network models, etc., can solve problems such as poor prediction ability, inability to follow the network model, single feature, etc., and achieve the effect of improving accuracy

Inactive Publication Date: 2017-04-05
数库(上海)科技有限公司
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  • Abstract
  • Description
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Problems solved by technology

[0005] The technical problem to be solved by the technical scheme of the present invention is that the existing named ent

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  • Method and system for identifying named entity
  • Method and system for identifying named entity

Examples

Experimental program
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Embodiment Construction

[0016] For the named entity recognition method of the embodiment of the present invention, please refer to figure 1 ,include:

[0017] Step S1, merging feature vectors;

[0018] Step S2, taking the combined feature vector as input, and processing it through the hidden layer, reduction layer and output classification layer of the neural network to obtain the classification output result;

[0019] Step S3, using a multi-pattern matching algorithm to identify the classification output results to obtain the target entity.

[0020] Combine below figure 2 and image 3 The examples shown are described in detail.

[0021] Step S1, merging feature vectors. Please refer to figure 1 and figure 2 , here a total of 5 words are input, the target entity is the most middle word to be predicted, and blank symbols are used to fill in the insufficient length. In this example, 4 feature vectors are used: the pre-trained word vector wordvec pretrain , self-trained word vector wordvec ra...

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Abstract

The technical scheme of the invention discloses a method and a system for identifying a named entity. The method for identifying the named entity comprises the following steps: merging feature vectors, wherein the feature vectors comprise pre-trained word vectors, self-training word vectors and part-of-speech tagging vectors, and a neural network is a convolutional neural network or a deep belief neural network; using the merged and obtained feature vectors as input, and obtaining a classified output result through treatment of a hidden layer, a reducing layer and an output classification layer of the neural network; and adopting a multi-mode matching algorithm to identify the classified output result so as to obtain the target entity. Through the technical scheme, the feature vectors are merged as input features of the neural network, so that the method can be well applied to specific classification scenes through treatment of the neural network and multi-mode matching.

Description

technical field [0001] The invention belongs to the technical field of language data processing, and in particular relates to a named entity recognition method and system. Background technique [0002] Named Entity Recognition (NER, Named Entity Recognition) refers to the recognition of entities with specific meaning in text or strings, mainly including names of people, places, institutions, proper nouns, etc. These identified entities can be applied to other natural language processing tasks, such as: dependency syntax, relation extraction, event extraction, etc. Therefore, the results of named entity recognition directly affect the subsequent natural language processing tasks. [0003] The existing named entity recognition methods mainly include dictionary matching, probabilistic graphical model, neural network and other methods. Dictionary matching adopts the method of manually adding user dictionaries, and the existing open source word segmentation toolkits provide int...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30G06N3/02
CPCG06F16/35G06F16/367G06F16/374G06F40/295G06N3/02
Inventor 张洛阳夏磊
Owner 数库(上海)科技有限公司
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