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Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
[article]
2019
arXiv
pre-print
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy. Domain adaptation methods enable leveraging labeled data from a different but related domain. However, different domains usually have various textual
arXiv:1908.08507v1
fatcat:3dicu6q6kvfxrex3pfr5rezvkm