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
- Get started (See our new tutorial for programmatic usage.)
- Motivation
- Sample output:
- Documentation tests: English
- UD Bank: English, Portuguese, and Spanish.
- Selected examples: Chinese, Portuguese, Spanish
- How PredPatt works: high-level overview, detailed list of rules
- Evaluation
- Related work
- References
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}
}