TY - GEN
T1 - Acquiring background knowledge to improve moral value prediction
AU - Lin, Ying
AU - Hoover, Joe
AU - Portillo-Wightman, Gwenyth
AU - Park, Christina
AU - Dehghani, Morteza
AU - Ji, Heng
N1 - Funding Information:
This work was supported by the U.S. ARL NS-CTA No. W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/10/24
Y1 - 2018/10/24
N2 -
We address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic textual features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. Our approach obtains 13.3% absolute F -score gains compared to our baseline model that only uses textual features.
1
1
Our code is available at https://github.com/limteng-rpi/mvp
AB -
We address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic textual features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. Our approach obtains 13.3% absolute F -score gains compared to our baseline model that only uses textual features.
1
1
Our code is available at https://github.com/limteng-rpi/mvp
KW - Background Knowledge
KW - Entity Linking
KW - Moral Value Prediction
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85057298585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057298585&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508244
DO - 10.1109/ASONAM.2018.8508244
M3 - Conference contribution
AN - SCOPUS:85057298585
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 552
EP - 559
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Y2 - 28 August 2018 through 31 August 2018
ER -