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Domain-aware Evaluation of Named Entity Recognition Systems for Croatian / Agić, Željko ; Bekavac, Božo.

By: Agić, Željko.
Contributor(s): Bekavac, Božo [aut].
Material type: materialTypeLabelArticleDescription: 1-15.ISSN: 1330-1136.Other title: Domain-aware Evaluation of Named Entity Recognition Systems for Croatian [Naslov na engleskom:].Subject(s): 5.04 | named entity recognition, Croatian language, text domain, domain dependence, evaluation hrv | named entity recognition, Croatian language, text domain, domain dependence, evaluation engOnline resources: Click here to access online In: CIT. Journal of computing and information technology 21 (2013), 3 ; str. 1-15Summary: We provide an evaluation of the currently available named entity recognition systems for Croatian. The evaluation puts special emphasis on domain dependence. To this goal, we manually annotated a dataset of approximately 1 million tokens of Croatian text from various domains within the newspaper text genre. The dataset was annotated using a three-class named entity tagset -- denoting personal names, locations and organizations. We give insight to feature selection, domain sensitivity and effects of increase in training set size for statistical named entity recognition using the state-of-the- art Stanford NER system. We also sketch a comparison of publicly available named entity recognition systems for Croatian considering domain dependence, regardless of their underlying paradigms. Our top-performing system achieved an F1 -score of 0.884 in a mixed-domain testing scenario, scoring 0.925 and 0.843 in the two domains separated for the experiment. The system shows consistency in state-of-the-art scores for detecting names of persons, locations and organizations.
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We provide an evaluation of the currently available named entity recognition systems for Croatian. The evaluation puts special emphasis on domain dependence. To this goal, we manually annotated a dataset of approximately 1 million tokens of Croatian text from various domains within the newspaper text genre. The dataset was annotated using a three-class named entity tagset -- denoting personal names, locations and organizations. We give insight to feature selection, domain sensitivity and effects of increase in training set size for statistical named entity recognition using the state-of-the- art Stanford NER system. We also sketch a comparison of publicly available named entity recognition systems for Croatian considering domain dependence, regardless of their underlying paradigms. Our top-performing system achieved an F1 -score of 0.884 in a mixed-domain testing scenario, scoring 0.925 and 0.843 in the two domains separated for the experiment. The system shows consistency in state-of-the-art scores for detecting names of persons, locations and organizations.

Projekt MZOS 130-1300646-1776

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