NER for Albanian Language: A Manually Annotated Corpus and Machine Learning Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A34R253AY" target="_blank" >RIV/00216208:11320/26:34R253AY - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-87769-8_14" target="_blank" >http://dx.doi.org/10.1007/978-3-031-87769-8_14</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-87769-8_14" target="_blank" >10.1007/978-3-031-87769-8_14</a>
Alternative languages
Result language
angličtina
Original language name
NER for Albanian Language: A Manually Annotated Corpus and Machine Learning Models
Original language description
Recent advancements in artificial intelligence (AI) have significantly enhanced tasks like named entity recognition (NER), enabling the identification of people, organizations, products, events, places, and dates. This paper introduces an Albanian NER corpus with 1,003,836 tokens (56,595 sentences), including 89,850 labelled tokens, annotated with 10 NER tags. The corpus, sourced from well-known Albanian news platforms, are used to train and evaluate 10 models using algorithms such as Naïve Bayes, Logistic Regression, SVM, Random Forest, Gradient Boosting, Extreme Gradient Boosting, and Multi-Layer Perceptron variants. Among these, Extra Trees and Random Forest achieved the best results, with approximately 96% accuracy and a 95% F1 score. As the largest NER corpus in Albanian, this resource advances linguistic research and AI applications, enhancing NER tasks and advance natural language processing (NLP) developments for the Albanian language. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Lecture Notes on Data Engineering and Communications Technologies
ISSN
23674512
e-ISSN
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Volume of the periodical
247
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
153-165
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105003097414