Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems
| dc.contributor.author | Padia, Ankur | |
| dc.contributor.author | Ferraro, Francis | |
| dc.contributor.author | Finin, Tim | |
| dc.date.accessioned | 2022-05-31T16:33:59Z | |
| dc.date.available | 2022-05-31T16:33:59Z | |
| dc.date.issued | 2022-05-27 | |
| dc.description | Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 42–52, Dublin, Ireland and Online. | |
| dc.description.abstract | Information extraction systems analyze text to produce entities and beliefs, but their output often has errors. In this paper, we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors. We consider both the scenario when the provenance text is automatically found by an information extraction system and when it is curated by humans. We contrast consistency with credibility; define and explore consistency and repair tasks; and demonstrate a simple yet effective and generalizable model. We analyze these tasks and evaluate this approach on three datasets. Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features. | en_US |
| dc.description.sponsorship | We would also like to thank the anonymous reviewers for their comments, questions, and suggestions. This material is based in part upon work supported by the National Science Foundation under Grant Nos. IIS-1940931, IIS-2024878, and DGE-2114892. Some experiments were conducted on the UMBC HPCF, supported by the National Science Foundation under Grant No. CNS1920079.This material is also based on research that is in part supported by the Army Research Laboratory, Grant No. W911NF2120076, and by the Air Force Research Laboratory (AFRL), DARPA, for the KAIROS program under agreement number FA8750-19-2-1003. The U.S.Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of the Air Force Research Laboratory (AFRL), DARPA, or the U.S. Government. | en_US |
| dc.description.uri | https://aclanthology.org/2022.deelio-1.5/ | en_US |
| dc.format.extent | 11 pages | en_US |
| dc.genre | conference papers and preceedings | en_US |
| dc.identifier | doi:10.13016/m2twfe-z8xc | |
| dc.identifier.citation | Ankur Padia, Francis Ferraro, and Tim Finin, Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems, 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 60th Annual Meeting of the Association for Computational Linguistics, May 2022. http://dx.doi.org/10.18653/v1/2022.deelio-1.5 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/24762 | |
| dc.identifier.uri | http://dx.doi.org/10.18653/v1/2022.deelio-1.5 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Association for Computational Linguistics | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.subject | UMBC Ebiquity Research Group | |
| dc.title | Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-6593-1792 | en_US |