ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Is it the right answer?: exploiting web redundancy for Answer Validation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Data-defined kernels for parse reranking derived from probabilistic models
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Freebase: a shared database of structured general human knowledge
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Re-ranking algorithms for name tagging
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
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
Margin-Based ranking meets boosting in the middle
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Search and mining entity-relationship data
Proceedings of the 20th ACM international conference on Information and knowledge management
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Using a Knowledge Base Population (KBP) slot filling task as a case study, we describe a re-ranking framework in the context of two experimental settings: (1) high transparency; a few pipelines share similar resources that can be used to provide the developer detailed intermediate answer results; (2) low transparency; many systems use diverse resources, and serve as black boxes, absent of any intermediate system results. In both settings, our results show that statistical re-ranking can effectively combine automated systems, achieving better performance than the best state-of-the-art individual system (6.6% absolute improvement in F-score) and alternative combination methods. Furthermore, to create labeled data for system development and assessment, information extraction tasks often require expensive human annotators to struggle with the vast amounts of information contained within a large scale corpus. In this paper, we demonstrate the impact of our learning-to-rank framework to combine output from multiple slot filling systems to populate entity-attribute facts in a knowledge base. We show that our approach can be used to create answer keys more efficiently and at a lower cost (63.5% reduction) than laborious human annotation.