Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.
|Title of host publication||ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||9|
|State||Published - 2020|
|Event||58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States|
Duration: 5 Jul 2020 → 10 Jul 2020
|Name||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|Conference||58th Annual Meeting of the Association for Computational Linguistics, ACL 2020|
|Period||5/07/20 → 10/07/20|
Bibliographical noteFunding Information:
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon2020 research and innovation programme, grant agreement 802774 (iEX-TRACT) and grant agreement 677352 (NLPRO).
© 2020 Association for Computational Linguistics