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An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entitiesстатья
- Авторы: Jiang Lei, Bolshakova Elena
- Сборник: Proceedings of Third Workshop "Computational linguistics and language science"
- Серия: EPiC Series in Language and Linguistics
- Том: 4
- Год издания: 2019
- Место издания: EasyChair
- Первая страница: 50
- Последняя страница: 60
- DOI: 10.29007/dp5m
- Аннотация: The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.
- Добавил в систему: Цзян Лэй