@article{tackstrom-etal-2015-efficient,
    title = "Efficient Inference and Structured Learning for Semantic Role Labeling",
    author = {T{\"a}ckstr{\"o}m, Oscar  and
      Ganchev, Kuzman  and
      Das, Dipanjan},
    editor = "Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/Q15-1003/",
    doi = "10.1162/tacl_a_00120",
    pages = "29--41",
    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."
}