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- W3215847282 abstract "This research paper focuses on developing a Named Entity Recognition (NER) system for a low-resource language, namely Azerbaijani. The paper develops NER models with two different approaches which are rule-based and machine learning-based approaches and compares the performances of them with familiar and unfamiliar datasets to determine the best approach. The rule-based approach uses statistics as its main technique and brings sufficient results - 70% f-score for both datasets. The second method consists of three models. The first one is obtained by training a model from scratch with Convolution Neural Network (CNN) using Spacy library which results in the best outcome - above 90% f-score for each test dataset. Secondly, a pre-trained multilingual Spacy model is also used to contrast the results, which proves the importance of the domain in which a NER model is trained since this model scored less than 50% in testing. Additionally, a new model has also been trained on top of the multilingual model using training dataset and performs the best in its domain." @default.
- W3215847282 created "2021-12-06" @default.
- W3215847282 creator A5080442403 @default.
- W3215847282 date "2021-10-13" @default.
- W3215847282 modified "2023-09-25" @default.
- W3215847282 title "Named Entity Recognition for the Azerbaijani Language" @default.
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- W3215847282 doi "https://doi.org/10.1109/aict52784.2021.9620336" @default.
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