PredPatt: Predicate-Argument Extraction from Universal Dependencies
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README.md

PredPatt: Predicate-Argument Extraction from Universal Dependencies

We present PredPatt, a framework of extensible, interpretable, language-neutral predicate-argument extraction patterns. PredPatt bridges the deep syntax of the Universal Dependency project to an initial shallow semantic layer: this can form the basis for future layering of semantic annotations atop Universal Dependency treebanks, and separately can be considered a linguistically well-founded component of a "Universal IE" mechanism.

PredPatt is part of a wider initiative on decompositional semantics at Johns Hopkins University. To that end, it has been used to bootstrap semantic annotations in our recent EMNLP 2016 paper (White et al., 2016).

PredPatt shows the best precision and recall when compared with several prominent Open IE tools on a large benchmark (Zhang et al., 2017).

PredPatt extracts predicates and arguments from text .

?a extracts ?b from ?c
    ?a: PredPatt
    ?b: predicates
    ?c: text
?a extracts ?b from ?c
    ?a: PredPatt
    ?b: arguments
    ?c: text

Table of contents

Citation

If you use PredPatt please cite it as follows.

@InProceedings{zhang-EtAl:2017:IWCS,
    author = {Zhang, Sheng and Rudinger, Rachel and {Van Durme}, Ben },
    title = {{An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling}},
    booktitle = {Proceedings of the 12th International Conference on Computational Semantics (IWCS)},
    month = {September},
    year = {2017},
    address = {Montpellier, France}
}

@InProceedings{white-EtAl:2016:EMNLP2016,
    author    = {White, Aaron Steven  and  Reisinger, Drew  and  Sakaguchi, Keisuke  and  Vieira, Tim  and  Zhang, Sheng  and  Rudinger, Rachel  and  Rawlins, Kyle  and  {Van Durme}, Benjamin},
    title     = {{Universal Decompositional Semantics on Universal Dependencies}},
    booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
    month     = {November},
    year      = {2016},
    address   = {Austin, Texas},
    publisher = {Association for Computational Linguistics},
    pages     = {1713--1723},
    url       = {https://aclweb.org/anthology/D16-1177}
}

License

BSD