Named Entity Recognition For Macedonian Language
Date Issued
2021-05-06
Author(s)
Ivan Krstev
Fisnik Doko
Abstract
Named Entity Recognition (NER), an outstanding
technique for information extraction from unstructured
texts, is lately becoming the central problem in
the field of Natural Language Processing (NLP). In the last
few years, multiple Python libraries, like SpaCy, NLTK
and FLAIR, accomplished state-of-the-art performances
for this problem. As NER is developing into a powerful
technique, its real-live applications are becoming more and
more numerous: from customer-message categorization to
ease of document analysis in greater corporations. In this
research, we use a ML-based system with the help of the
FLAIR library in Python, which has already provided
optimal results for NER in few world-class languages
(English, German, Russian, French etc.), for financial
entity recognition in financial texts written in Macedonian
language. For the NER task on 13 distinct labels using
our dataset in Macedonian language on the proposed ML
model we have obtained F1-score of around 0.75.
technique for information extraction from unstructured
texts, is lately becoming the central problem in
the field of Natural Language Processing (NLP). In the last
few years, multiple Python libraries, like SpaCy, NLTK
and FLAIR, accomplished state-of-the-art performances
for this problem. As NER is developing into a powerful
technique, its real-live applications are becoming more and
more numerous: from customer-message categorization to
ease of document analysis in greater corporations. In this
research, we use a ML-based system with the help of the
FLAIR library in Python, which has already provided
optimal results for NER in few world-class languages
(English, German, Russian, French etc.), for financial
entity recognition in financial texts written in Macedonian
language. For the NER task on 13 distinct labels using
our dataset in Macedonian language on the proposed ML
model we have obtained F1-score of around 0.75.
Subjects
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