Abstract—One of the most important factors which directly
and significantly affects the quality of the neural sequence
labeling is the selection and encoding the input features to
generate rich semantic and grammatical representation vectors.
In this paper, we propose a deep neural network model to
address a particular task of sequence labeling problem, the task
of Named Entity Recognition (NER). The model consists of
three sub-networks to fully exploit character-level and
capitalization features as well as word-level contextual
representation. To show the ability of our model to generalize to
different languages, we evaluated the model in Russian,
Vietnamese, English and Chinese and obtained state-of-the-art
performances: 91.10%, 94.43%, 91.22%, 92.95% of F-Measure
on Gareev's dataset, VLSP-2016, CoNLL-2003 and MSRA
datasets, respectively. Besides that, our model also obtained a
good performance (about 70% of F1) with using only 100
samples for training and development sets.
Index Terms—Named entity recognition, bi-directional long short-term memory, convolutional neural network, conditional random field.
The Anh Le is with Neural Networks and Deep Learning Lab, Moscow Institute of Physics and Technology, Russia. He is also with Faculty of Information Technology, Vietnam Maritime University, Viet Nam (e-mail: email@example.com).
Mikhail S. Burtsev is with Neural Networks and Deep Learning Lab, Moscow Institute of Physics and Technology, Russia (e-mail: firstname.lastname@example.org).
Cite: The Anh Le and Mikhail S. Burtsev, "A Deep Neural Network Model for the Task of Named Entity Recognition," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 8-13, 2019.