  Document-level relation extraction (RE) aims to identify relations between
entities across multiple sentences. Most previous methods focused on
document-level RE under full supervision. However, in real-world scenario, it
is expensive and difficult to completely label all relations in a document
because the number of entity pairs in document-level RE grows quadratically
with the number of entities. To solve the common incomplete labeling problem,
we propose a unified positive-unlabeled learning framework - shift and squared
ranking loss positive-unlabeled (SSR-PU) learning. We use positive-unlabeled
(PU) learning on document-level RE for the first time. Considering that labeled
data of a dataset may lead to prior shift of unlabeled data, we introduce a PU
learning under prior shift of training data. Also, using none-class score as an
adaptive threshold, we propose squared ranking loss and prove its Bayesian
consistency with multi-label ranking metrics. Extensive experiments demonstrate
that our method achieves an improvement of about 14 F1 points relative to the
previous baseline with incomplete labeling. In addition, it outperforms
previous state-of-the-art results under both fully supervised and extremely
unlabeled settings as well.
