@article{TACL465,
	author = {Oscar Täckström and Kuzman Ganchev and Dipanjan Das},
	title = {Efficient Inference and Structured Learning for Semantic Role Labeling},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {3},
	year = {2015},
	keywords = {},
	abstract = {We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora. },
	issn = {2307-387X},
	url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/465},
	pages = {29--41}
}