Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3529-3535.doi: 10.13229/j.cnki.jdxbgxb.20221137

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Fast recognition method of short text named entities considering feature sparsity

Yue-kun MA1,2,3,4(),Yi-feng HAO1   

  1. 1.College of Artificial Intelligence，North China University of Science and Technology，Tangshan 063210，China
    2.Hebei Provincial Key Laboratory of Industrial Intelligent Perception，North China University of Science and Technology，Tangshan 063210，China
    3.School of Computer & Communication Engineering，University of Science & Technology，Beijing 100083，China
    4.Beijing Key Laboratory of Knowledge Engineering for Materials Science，University of Science & Technology Beijing，Beijing 100083，China
  • Received:2022-10-12 Online:2023-12-01 Published:2024-01-12

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Abstract:

The proposed method selects appropriate features by filtering punctuation marks， constructs vectors， segments two or more words to form specific semantics， and labels parts of speech to identify relative parts of speech； Utilizing the Latent dirichlet allocation （LDA） model to establish correlation between topics and documents， clarify document topics， and reduce data feature sparsity； The Bidirectional long short-term memory-conditional random field （BR-BiLSTM-CRF）model detects the boundaries of text named entities through a bidirectional LSTM model， which is combined with the output entity types of the chain conditional random field layer. After adding features of vocabulary and parts of speech， the overall sequence entity edge of the text is detected. The network parameters are corrected using cross entropy and gradient descent until the error does not exceed the specified value， achieving text named entity recognition. Through experiments， it has been proven that the proposed method has fast recognition speed， high accuracy， and strong overall performance. The proposed method can better recognize language through computers， clarify the part of speech of text， and improve the accuracy and efficiency of named entity recognition.

Key words: natural language processing, feature sparsity, short text naming, fast recognition of short text entities, text preprocessing, characteristic weight

CLC Number: 

  • TP391.1

Table 1

Punctuation filtering table"

名称样式名称样式
单位符号￥＄℃￠￡叹号！！
百分号、千分号%、‰左括号））｛［【《<
破折号——右括号））｝］】》>
省略号…...左引号“‘
冒号：右引号”’
顿号、句号。
分号；；问号？？
逗号，，

Table 2

Part of speech abbreviations"

虚词词性缩写实词词性缩写
介词p动词v
副词d名词n
助词u形容词a
叹词e量词q
语气词o数词m
连词c代词r

Fig.1

LDA probability model"

Fig.2

BR BiLSTM CRF model"

"

名称样本数
人名地名组织名其他
MSRA数据集15218754791017
OntoNotes数据集104210843281108
Iris数据集80448111931054

Fig.3

Comparison of three methods under MSRA dataset"

Fig.4

Comparison of three methods under OntoNotes dataset"

Fig.5

Comparison of three methods under Iris dataset"

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