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

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS

AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-02-12 DOI:10.3103/S0146411624700391

M. D. Averina, O. A. Levanova

引用次数: 0

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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来源期刊

AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.70

自引率

22.20%

发文量

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