Practical Comparative Analysis of Named Entity Recognition Methods for JINR Digital Services

Efficient extraction of named entities is essential for data analysis, especially given the increasing volume of textual information. This study examines and compares methods for extracting named entities within text-processing algorithm for excursion data, offering statistical insights into events described in natural language texts, which include brief information about the excursion name and its target audience. The practical results highlight the applicability of these methods in supporting and enhancing JINR’s internal services, particularly the JINRex system for excursion planning and management.

Authors

Institutions

Publication Details

Journal
Physics of Particles and Nuclei Letters
Published
2025-10-01
DOI
https://doi.org/10.1134/s1547477125701031
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Practical Comparative Analysis of Named Entity Recognition Methods for JINR Digital Services

А. И. Ильина
Physics of Particles and Nuclei Letters
Topic Modeling
article

Practical Comparative Analysis of Named Entity Recognition Methods for JINR Digital Services

А. И. Ильина
article en

Abstract

Efficient extraction of named entities is essential for data analysis, especially given the increasing volume of textual information. This study examines and compares methods for extracting named entities within text-processing algorithm for excursion data, offering statistical insights into events described in natural language texts, which include brief information about the excursion name and its target audience. The practical results highlight the applicability of these methods in supporting and enhancing JINR’s internal services, particularly the JINRex system for excursion planning and management.

Physics of Particles and Nuclei LettersVol. 22(5)
Joint Institute for Nuclear Research (RU)
Openalex Percentile: Top 86%
Topic Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.