TY  - CONF
ID  - SisLab4180
UR  - https://kse-conference.org/
A1  - Nguyen, Thi Cham
A1  - Pham, Thi Ngan
A1  - Le, Hoang Quynh
A1  - Nguyen, Tri Thanh
A1  - Bui, Hong Nhung
A1  - Ha, Quang Thuy
Y1  - 2020/11/12/
N2  - Recently, deep Convolutional Neural Network (CNN) model has achieved remarkable results in Natural Language Processing (NLP) tasks, such as information retrieval, relation classification, semantic parsing, sentence modeling and other traditional NLP tasks, etc. On the other hand, topic modeling method has been proved to be effective by exploiting hidden knowledge in a corpus of documents. Motivated from these successes, we propose a framework that takes advantages of closure domain measure to get enriched knowledge from close domains to the dataset of the current task to improve the CNN model, and apply a Targeted Topic Model to take more detailed exploration on each labeled aspect of an opinion. Experimental results on different scenarios show the effectiveness of the proposed framework for multi-label classification task in comparison to other related models on the same Hotel review dataset.
TI  - A Targeted Topic Model based Multi-Label 
Deep Learning Classification Framework 
for Aspect-based Opinion Mining
M2  - C?n Th?, Vi?t Nam
AV  - public
T2  - The 12th IEEE International Conference on KNOWLEDGE AND SYSTEMS ENGINEERING ( KSE 2020)
ER  -