TY  - CONF
ID  - SisLab3642
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3642/
A1  - Luong, Thai Le
A1  - Tran, Nhu Thuat
A1  - Phan, Xuan Hieu
Y1  - 2019/09/25/
N2  - User intent extraction from social media texts is aimed at identifying user intent keyword and its related information. This topic has attracted a lot of researches since its various applications in online marketing, e-commerce and business services. One of such studies is to model this problem as a sequence labeling task and apply state-of-the-art sequential tagging models such as BiLSTM [12] and BiLSTM-CRFs [12]. In this paper, we take a further step to enhance intent extraction results based on tri-training [23] and ensemble learning [2]. Specifically, we simultaneously use three BiLSTM-CRFs models, each of them is different from others by the type of word embeddings, and apply majority voting scheme over their predicted labels when decoding final labels. Extensive experiments on data from three domains Real Estate, Tourism and Transportation show that our proposed methods enjoy a better performance compared to single model based approach.
TI  - Improving Intent Extraction Using Ensemble Neural Network
SP  - 58
M2  - Ho Chi Minh City, Vietnam
AV  - none
EP  - 63
T2  - The 19th International Symposium on Communications and Information Technologies (ISCIT 2019)
ER  -