[1]杜睿山,陈思路,刘文豪.基于岩石文本信息的命名实体识别[J].计算机技术与发展,2022,32(09):188-192.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 029]
　DU Rui-shan,CHEN Si-lu,LIU Wen-hao.Named Entity Recognition Based on Rock Text Information[J].,2022,32(09):188-192.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 029]
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基于岩石文本信息的命名实体识别()
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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:
188-192
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 Rock Text Information
文章编号:
1673-629X(2022)09-0188-05
作者:
杜睿山; 陈思路; 刘文豪
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
DU Rui-shan; CHEN Si-lu; LIU Wen-hao
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
命名实体识别; Lexicon; 岩石; 非结构化文本; 条件随机场; 知识抽取
Keywords:
named entity recognition; Lexicon; rock; unstructured text; conditional random field; knowledge extraction
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 09. 029
摘要:
命名实体识别技术是自然语言处理领域的重要任务之一。 但岩石文本信息中的命名实体存在边界不清、分词困难、误差传播、计算效率慢等问题。 基于岩石文本信息进行知识抽取对油气勘探领域的研究具有重大意义。 为此,该文首先构建岩石文本数据集,并提出 Lexicon-BiLSTM-CRF 网络模型应用于非结构化的岩石文本上,该模型首先经过 Lexicon机制获得每个字符的所有匹配词,从而解决了边界不清、分词困难的问题,在此基础上提升了计算效率。 然后通过双向长短期记忆网络( BiLSTM) 提取上下文语义特征,将语义向量传入条件随机场(CRF) 层并采用维特比算法解码,降低了错误标签的输出概率并预测实体标注标签,最终实现岩石文本的命名实体抽取任务。 在构建的岩石文本数据集的基础上进行几组对比实验,验证了该方法在准确率和召回率上具有一定提升。
Abstract:
Named entity recognition technology is one of the important tasks in the field of natural language processing. However,the named entities in the rock text information have problems such as unclear boundaries,difficult word segmentation,error propagation,and slow calculation efficiency. Knowledge extraction based on rock text information is of great significance to the research in the field of oiland gas exploration. To this end,we first build a rock text data set,and then propose a Lexicon - LSTM - CRF network model to be applied to unstructured rock text. Firstly,the Lexicon mechanism is used to obtain all matching words of each character,so as to solve theproblem of unclear boundary and difficult word segmentation, and on this basis, improve the computational efficiency. Then the contextual semantic features are extracted through the bidirectional long-term short-term memory network ( BiLSTM) ,and the semanticvector is passed into the Conditional Random Field ( CRF) layer and decoded by the Viterbi algorithm to reduce the output probability of the error label and predict the entity annotation label,and finally realize the rock text Named entity extraction task. Through several comparative experiments on the rock text data set constructed,it is verified that the proposed method has a certain improvement in accuracyand recall.

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更新日期/Last Update: 2022-09-10