Abstract
We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.
| Original language | English |
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| Title of host publication | Long Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 885-895 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781948087278 |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 |
Publication series
| Name | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference |
|---|---|
| Volume | 1 |
Conference
| Conference | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 1/06/18 → 6/06/18 |
Bibliographical note
Publisher Copyright:© 2018 The Association for Computational Linguistics.