[1]孙安亮,时宏伟,王金策.基于字符与单词嵌入的航空安全命名实体识别[J].计算机技术与发展,2022,32(09):148-153.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 023]
　SUN An-liang,SHI Hong-wei,WANG Jin-ce.Named Entity Recognition Based on Character and Word Embedding in Aviation Safety[J].,2022,32(09):148-153.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 023]
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基于字符与单词嵌入的航空安全命名实体识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年09期
页码:
148-153
栏目:
新型计算应用系统
出版日期:
2022-09-10

文章信息/Info

Title:
Named Entity Recognition Based on Character and Word Embedding in Aviation Safety
文章编号:
1673-629X(2022)09-0148-06
作者:
孙安亮; 时宏伟; 王金策
四川大学 计算机学院,四川 成都 610000
Author(s):
SUN An-liang; SHI Hong-wei; WANG Jin-ce
School of Computer,Sichuan University,Chengdu 610000,China
关键词:
命名实体识别; 双向长短期记忆网络; 卷积神经网络; 条件随机场; 航空安全
Keywords:
named entity recognition;  bidirectional long short - term memory;  convolutional neural network;  conditional random field; aviation safety
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 09. 023
摘要:
航空安全命名实体识别是构建航空安全知识图谱中基础且关键的任务,对消除航空隐患,制定有效的纠正措施和宏观政策提供了重要依据。 针对航空安全领域包含大量较长的专有名词和名词缩写混合等问题,采用双向长短期记忆模型(BILSTM) 、卷积神经网络( CNN) 和条件随机场( CRF) ,构建一种使用字符与词两个粒度的模型,对航空安全事故进行命名实体识别( NER),以提取事故中的实体。 采用航空事故报道为实验数据集,利用 BILSTM 模型自动学习字符粒度的语义特征向量,再结合词粒度的特征向量,通过 CNN 全局特征,最后通过 CRF 层对提取到的特征进行序列标注,以提取命名实体。 经过实验对比验证,该模型能够有效提取命名实体,F1 值相对现有方法提升了 2. 22% 。 实验结果表明,增加字符粒度的嵌入并且使用 CNN 获取全局特征可以有效提高航空安全领域命名实体识别效果。
Abstract:
The identification of aviation safety nomenclature is a basic and critical task in the construction of aviation safety knowledge map,which provides an important basis for eliminating aviation hidden dangers and formulating effective corrective measures and macro policies. Aimed at locally sensitive problems such as the mixture of a large number of long proper nouns and noun abbreviations,we use bidirectional long - term short - term memory model ( BILSTM) , convolutional neural network ( CNN) and conditional random field( CRF) to construct a model that uses two granularities of characters and words to perform named entity recognition ( NER) for aviation safety accidents to extract the entity in the accident. The aviation accident report is used as the experimental data set,and the BILSTM model is used to learn the semantic feature vector of the character granularity. Combined with the feature vector of the word gran ularity,the global feature is obtained through CNN,and finally the extracted features are sequenced through the CRF layer to extract the name entity. After experimental comparison and verification,the model can effectively extract named entities and the F1 value is increased by 2. 22% compared with the existing methods. Experimental results show that increasing the embedding of character granularity and using CNN to obtain global features can effectively improve the effect of named entity recognition in the aviation safety field.

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更新日期/Last Update: 2022-09-10