Uyghur named entity recognition method based on depth learning

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...

AI Technical Summary

Problems solved by technology

However, there is no recognition method with a relatively high recognition rate based on deep learning, which still cannot meet the application requirements.

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