TY - GEN
T1 - Knowledge-enriched two-layered attention network for sentiment analysis
AU - Kumar, Abhishek
AU - Kawahara, Daisuke
AU - Kurohashi, Sadao
N1 - Funding Information:
This work was supported by the Kyoto University Cooperative Intelligence Short-term Internship Program.
Publisher Copyright:
© 2018 Association for Computational Linguistics.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the Word- Net. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis. We evaluate our model on the benchmark dataset of SemEval 2017 Task 5. Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5. The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
AB - We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the Word- Net. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis. We evaluate our model on the benchmark dataset of SemEval 2017 Task 5. Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5. The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
UR - http://www.scopus.com/inward/record.url?scp=85059660544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059660544&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059660544
T3 - NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
SP - 253
EP - 258
BT - Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Y2 - 1 June 2018 through 6 June 2018
ER -