  Relation extraction (RE) consists in categorizing the relationship between
entities in a sentence. A recent paradigm to develop relation extractors is
Distant Supervision (DS), which allows the automatic creation of new datasets
by taking an alignment between a text corpus and a Knowledge Base (KB). KBs can
sometimes also provide additional information to the RE task. One of the
methods that adopt this strategy is the RESIDE model, which proposes a
distantly-supervised neural relation extraction using side information from
KBs. Considering that this method outperformed state-of-the-art baselines, in
this paper, we propose a related approach to RESIDE also using additional side
information, but simplifying the sentence encoding with BERT embeddings.
Through experiments, we show the effectiveness of the proposed method in Google
Distant Supervision and Riedel datasets concerning the BGWA and RESIDE baseline
methods. Although Area Under the Curve is decreased because of unbalanced
datasets, P@N results have shown that the use of BERT as sentence encoding
allows superior performance to baseline methods.
