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Type of publication: research article
Type of publication (PDB): Straipsnis kitose duomenų bazėse / Article in other databases (S4)
Field of Science: Informatika / Informatics (N009)
Author(s): Mandravickaitė, Justina;Krilavičius, Tomas
Title: Testing performance of NER models for Russian
Is part of: IJDATICS: International journal of design, analysis and tools for integrated circuits and systems. Hong Kong: Solari (HK) Co, 2021, Vol. 10, iss. 1
Extent: p. 55-58
Date: 2021
Keywords: NER;Model comparison;Russian named entities;Named Entity Recognition
Abstract: This paper describes an experiment in testing 3 NER models (spaCy, Stanza and DeepPavlov) for Russian. The models were tested on WikiAnn Russian subset. Standard evaluation metrics (Precision, Recall and F-score) were complemented with 2 inter-annotator agreement measures (Fleiss’ κ and Krippendorff’s α). DeepPavlov performed better than the other 2 models in terms of all 3 standard performance measures. Stanza performed better than spaCy in terms of precision, while they shared the same Fmeasure score. Both Fleiss’ κ and Krippendorff’s α resulted in an only moderate agreement, showing, that our selected 3 NER models only moderately agree in terms of annotation of Russian named entities
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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