  In this study, a novel method for extracting named entities and relations
from unstructured text based on the table representation is presented. By using
contextualized word embeddings, the proposed method computes representations
for entity mentions and long-range dependencies without complicated
hand-crafted features or neural-network architectures. We also adapt a tensor
dot-product to predict relation labels all at once without resorting to
history-based predictions or search strategies. These advances significantly
simplify the model and algorithm for the extraction of named entities and
relations. Despite its simplicity, the experimental results demonstrate that
the proposed method outperforms the state-of-the-art methods on the CoNLL04 and
ACE05 English datasets. We also confirm that the proposed method achieves a
comparable performance with the state-of-the-art NER models on the ACE05
datasets when multiple sentences are provided for context aggregation.
