Uyghur named entity recognition method based on depth learning

A technology of named entity recognition and deep learning, applied in the fields of instruments, computing, electrical digital data processing, etc., can solve problems such as inability to meet application requirements, and achieve the effect of solving named entity labeling problems and a wide range of application scenarios.

Pending Publication Date: 2019-01-01
XINJIANG UNIVERSITY
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  • Application Information

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Problems solved by technology

However, there is no recognition method with a relatively high recognition rat

Method used

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  • Uyghur named entity recognition method based on depth learning
  • Uyghur named entity recognition method based on depth learning
  • Uyghur named entity recognition method based on depth learning

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Embodiment Construction

[0024] As shown in the figure, the Uighur named entity recognition method based on deep learning of the present embodiment includes the following steps:

[0025] (1) Carry out sentence segmentation and word segmentation for the Uighur text data to be marked, and perform character extraction and syllable segmentation for words;

[0026] (2) Use a bidirectional LSTM network to obtain forward and reverse character vectors respectively for the extracted characters, and splice them together to form a character vector representation of words;

[0027] (3) Use a bidirectional LSTM network to obtain forward and reverse syllable vectors respectively for the segmented syllables, and splicing them together to form a syllable vector representation of words;

[0028] (4) Splicing character vectors, syllable vectors and word vectors and passing them to the bidirectional LSTM neural network to train the information features of the input sentences;

[0029] (5) Based on the output obtained i...

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Abstract

The invention discloses a Uyghur named entity recognition method based on depth learning. The method comprises the following steps: (1) segmenting Uyghur text, respectively extracting characters and segmenting syllables; (2) obtaining forward and reverse character vectors from extracted characters by bi-directional LSTM network, and splicing them together to form character vector representations of words; (3) obtaining forward and reverse syllable vectors from segmented syllables by bi-directional LSTM network, and splicing them together to form syllable vector representations of words; (4) splicing the character vector, syllable vector and word vector and modeling the context information of each word as a bi-directional LSTM neural network; (5) at the output of LSTM neural network, the whole sentence is labeled with named entity by using conditional random field. The invention extracts the abundant structure information of words by taking the splicing of characters, syllables and wordvectors as the input of the neural network, so that the invention can be widely applied in the sequence labeling of the morphologically rich languages.

Description

technical field [0001] The present invention relates to natural language processing, in particular to a Uyghur named entity recognition method based on deep learning. Background technique [0002] With the rapid development of Internet technology, search engines and translation systems for minority languages ​​in Xinjiang have developed relatively well, but in terms of Uyghur named entity recognition, there is still a lack of high-accuracy named entity recognition methods. Named entity recognition, as the basis of machine translation, information extraction and retrieval, has important research significance. [0003] There are currently three named entity recognition (Named Entity Recognition, NER) methods: rule-based methods, statistical methods and neural network-based methods. The basic idea of ​​rule-based named entity recognition is to manually write context-sensitive productions, use ordinary named entity (NE) databases, and assign different weights to different rules...

Claims

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Application Information

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IPC IPC(8): G06F17/27
CPCG06F40/295
Inventor 买合木提·买买提艾山·吾买尔吐尔根·依布拉音王路路卡哈尔江·阿比的热西提
Owner XINJIANG UNIVERSITY
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