Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Knowledge base population: successful approaches and challenges
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Combining flat and structured approaches for temporal slot filling or: how much to compress?
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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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.