TY - GEN
T1 - Improving short text modeling by two-level attention networks for sentiment classification
AU - Li, Yulong
AU - Cai, Yi
AU - Leung, Ho Fung
AU - Li, Qing
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Understanding short texts is crucial to many applications, but it has always been challenging, due to the sparsity and ambiguity of information in short texts. In addition, sentiments expressed in those user-generated short texts are often implicit and context dependent. To address this, we propose a novel model based on two-level attention networks to identify the sentiment of short text. Our model first adopts attention mechanism to capture both local features and long-distance dependent features simultaneously, so that it is more robust against irrelevant information. Then the attention-based features are non-linearly combined with a bidirectional recurrent attention network, which enhances the expressive power of our model and automatically captures more relevant feature combinations. We evaluate the performance of our model on MR, SST-1 and SST-2 datasets. The experimental results show that our model can outperform the previous methods.
AB - Understanding short texts is crucial to many applications, but it has always been challenging, due to the sparsity and ambiguity of information in short texts. In addition, sentiments expressed in those user-generated short texts are often implicit and context dependent. To address this, we propose a novel model based on two-level attention networks to identify the sentiment of short text. Our model first adopts attention mechanism to capture both local features and long-distance dependent features simultaneously, so that it is more robust against irrelevant information. Then the attention-based features are non-linearly combined with a bidirectional recurrent attention network, which enhances the expressive power of our model and automatically captures more relevant feature combinations. We evaluate the performance of our model on MR, SST-1 and SST-2 datasets. The experimental results show that our model can outperform the previous methods.
UR - http://www.scopus.com/inward/record.url?scp=85048037695&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91452-7_56
DO - 10.1007/978-3-319-91452-7_56
M3 - Conference article published in proceeding or book
AN - SCOPUS:85048037695
SN - 9783319914510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 878
EP - 890
BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
A2 - Manolopoulos, Yannis
A2 - Li, Jianxin
A2 - Sadiq, Shazia
A2 - Pei, Jian
PB - Springer-Verlag
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Y2 - 21 May 2018 through 24 May 2018
ER -