Abstract
Document-level joint entity and relation extraction is a challenging information extraction problem that requires a unified approach where a single neural network performs four sub-tasks: mention detection, coreference resolution, entity classification, and relation extraction. Existing methods often utilize a sequential multi-task learning approach, in which the arbitral decomposition causes the current task to depend only on the previous one, missing the possible existence of the more complex relationships between them. In this paper, we present a multi-task learning framework with bidirectional memory-like dependency between tasks to address those drawbacks and perform the joint problem more accurately. Our empirical studies show that the proposed approach outperforms the existing methods and achieves state-of-the-art results on the BioCreative V CDR corpus.
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Acknowledgements
The research was conducted under the Implementation Doctorate programme of Polish Ministry of Science and Higher Education and also partially funded by Department of Artificial Intelligence, Wroclaw Tech and by the European Union under the Horizon Europe grant OMINO (grant number 101086321). It was also partially co-funded by the European Regional Development Fund within Measure 1.1. “Enterprise R &D Projects”, Sub-measure 1.1.1. “Industrial research and development by companies” as part of The Operational Programme Smart Growth 2014-2020, support contract no. POIR.01.01.01-00-0876/20-00.
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Kościukiewicz, W., Wójcik, M., Kajdanowicz, T., Gonczarek, A. (2023). Similarity-Based Memory Enhanced Joint Entity and Relation Extraction. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_29
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DOI: https://doi.org/10.1007/978-3-031-36021-3_29
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