TY - GEN
T1 - Relabeling Distantly Supervised Training Data for Temporal Knowledge Base Population
AU - Tamang, Suzanne
AU - Ji, Heng
N1 - Funding Information:
This work was supported by the U.S. Army Research Laboratory under Cooperative Agreement No. W911NF- 09-2-0053 (NS-CTA), the U.S. NSF CAREER Award under Grant IIS-0953149, the U.S. NSF EAGER Award under Grant No. IIS-1144111, the U.S. DARPA Broad Operational Language Translations program and PSC-CUNY Research Program. 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.
Funding Information:
This work was supported by the U.S. Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA), the U.S. NSF CAREER Award under Grant IIS-0953149, the U.S. NSF EAGER Award under Grant No. IIS-1144111, the U.S. DARPA Broad Operational Language Translations program and PSC-CUNY Research Program. 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:
© 2012 Association for Computational Linguistics
PY - 2012
Y1 - 2012
N2 - We enhance a temporal knowledge base population system to improve the quality of distantly supervised training data and identify a minimal feature set for classification. The approach uses multi-class logistic regression to eliminate individual features based on the strength of their association with a temporal label followed by semi-supervised relabeling using a subset of human annotations and lasso regression. As implemented in this work, our technique improves performance and results in notably less computational cost than a parallel system trained on the full feature set.
AB - We enhance a temporal knowledge base population system to improve the quality of distantly supervised training data and identify a minimal feature set for classification. The approach uses multi-class logistic regression to eliminate individual features based on the strength of their association with a temporal label followed by semi-supervised relabeling using a subset of human annotations and lasso regression. As implemented in this work, our technique improves performance and results in notably less computational cost than a parallel system trained on the full feature set.
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M3 - Conference contribution
AN - SCOPUS:84929238986
T3 - Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction, AKBC-WEKEX 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012
SP - 25
EP - 30
BT - Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction, AKBC-WEKEX 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Fan, James
A2 - Hoffman, Raphael
A2 - Kalyanpur, Aditya
A2 - Riedel, Sebastian
A2 - Suchanek, Fabian
A2 - Talukdar, Partha Pratim
PB - Association for Computational Linguistics (ACL)
T2 - 2021 Joint Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction, AKBC-WEKEX 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012
Y2 - 7 June 2012 through 8 June 2012
ER -