  Single implementing, concatenating, adding or replacing of the
representations has yielded significant improvements on many NLP tasks. Mainly
in Relation Extraction where static, contextualized and others representations
that are capable of explaining word meanings through the linguistic features
that these incorporates. In this work addresses the question of how is improved
the relation extraction using different types of representations generated by
pretrained language representation models. We benchmarked our approach using
popular word representation models, replacing and concatenating static,
contextualized and others representations of hand-extracted features. The
experiments show that representation is a crucial element to choose when DL
approach is applied. Word embeddings from Flair and BERT can be well
interpreted by a deep learning model for RE task, and replacing static word
embeddings with contextualized word representations could lead to significant
improvements. While, the hand-created representations requires is
time-consuming and not is ensure a improve in combination with others
representations.
