@InProceedings{mcdowell-EtAl:2017:I17-1,
  author    = {McDowell, Bill  and  Chambers, Nathanael  and  Ororbia II, Alexander  and  Reitter, David},
  title     = {Event Ordering with a Generalized Model for Sieve Prediction Ranking},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {843--853},
  abstract  = {This paper improves on several aspects of
	a sieve-based event ordering architecture,
	CAEVO (Chambers et al., 2014), which
	creates globally consistent temporal relations
	between events and time expressions.
	First, we examine the usage of word embeddings
	and semantic role features. With
	the incorporation of these new features, we
	demonstrate a 5% relative F1 gain over our
	replicated version of CAEVO. Second, we
	reformulate the architecture’s sieve-based
	inference algorithm as a prediction reranking
	method that approximately optimizes a
	scoring function computed using classifier
	precisions. Within this prediction reranking
	framework, we propose an alternative
	scoring function, showing an 8.8% relative
	gain over the original CAEVO. We further
	include an in-depth analysis of one of
	the main datasets that is used to evaluate
	temporal classifiers, and we show how despite
	using the densest corpus, there is still
	a danger of overfitting. While this paper
	focuses on temporal ordering, its results
	are applicable to other areas that use sieve-based
	architectures.},
  url       = {http://www.aclweb.org/anthology/I17-1085}
}