  Cross-document Relation Extraction aims to predict the relation between
target entities located in different documents. In this regard, the dominant
models commonly retain useful information for relation prediction via bridge
entities, which allows the model to elaborately capture the intrinsic
interdependence between target entities. However, these studies ignore the
non-bridge entities, each of which co-occurs with only one target entity and
offers the semantic association between target entities for relation
prediction. Besides, the commonly-used dataset--CodRED contains substantial NA
instances, leading to the prediction bias during inference. To address these
issues, in this paper, we propose a novel graph-based cross-document RE model
with non-bridge entity enhancement and prediction debiasing. Specifically, we
use a unified entity graph to integrate numerous non-bridge entities with
target entities and bridge entities, modeling various associations between
them, and then use a graph recurrent network to encode this graph. Finally, we
introduce a novel debiasing strategy to calibrate the original prediction
distribution. Experimental results on the closed and open settings show that
our model significantly outperforms all baselines, including the GPT-3.5-turbo
and InstructUIE, achieving state-of-the-art performance. Particularly, our
model obtains 66.23% and 55.87% AUC points in the official
leaderboard\footnote{\url{https://codalab.lisn.upsaclay.fr/competitions/3770#results}}
under the two settings, respectively, ranking the first place in all
submissions since December 2023. Our code is available at
https://github.com/DeepLearnXMU/CoRE-NEPD.
