Extracting Named Entities from Russian-Language Documents with Varying Degrees of Structural Clarity

IF 0.5 Q4 AUTOMATION & CONTROL SYSTEMS AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-02-12 DOI:10.3103/S0146411624700391
M. D. Averina, O. A. Levanova
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Abstract

This study addresses the task of recognizing named entities in Russian texts using the CRF model. We analyze two datasets: well-structured refinancing documents and loosely structured court transcripts. We test the model with various text features and CRF parameters (optimization algorithms). On average, the best F-measure for well-structured documents is 0.99, while for loosely structured ones, it is 0.86.

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从结构清晰程度不同的俄语文档中提取命名实体
本研究解决了使用CRF模型识别俄语文本中命名实体的任务。我们分析了两个数据集:结构良好的再融资文件和结构松散的法庭记录。我们用各种文本特征和CRF参数(优化算法)测试模型。平均而言,结构良好的文档的最佳f度量值为0.99,而结构松散的文档的最佳f度量值为0.86。
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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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