A Comparative Study of Neural Network Models for Sentence Classification

Autor:	Le-Hong, Phuong, Le, Anh-Cuong
Rok vydání:	2018
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	This paper presents an extensive comparative study of four neural network models, including feed-forward networks, convolutional networks, recurrent networks and long short-term memory networks, on two sentence classification datasets of English and Vietnamese text. We show that on the English dataset, the convolutional network models without any feature engineering outperform some competitive sentence classifiers with rich hand-crafted linguistic features. We demonstrate that the GloVe word embeddings are consistently better than both Skip-gram word embeddings and word count vectors. We also show the superiority of convolutional neural network models on a Vietnamese newspaper sentence dataset over strong baseline models. Our experimental results suggest some good practices for applying neural network models in sentence classification. Comment: To appear in the 5th NAFOSTED Conference on Information and Computer Science
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.01656 Zobrazit plný text záznamu View this record from Arxiv