  Extracting entity pairs along with relation types from unstructured texts is
a fundamental subtask of information extraction. Most existing joint models
rely on fine-grained labeling scheme or focus on shared embedding parameters.
These methods directly model the joint probability of multi-labeled triplets,
which suffer from extracting redundant triplets with all relation types.
However, each sentence may contain very few relation types. In this paper, we
first model the final triplet extraction result as a three-order tensor of
word-to-word pairs enriched with each relation type. And in order to obtain the
sentence contained relations, we introduce an independent but joint training
relation classification module. The tensor decomposition strategy is finally
utilized to decompose the triplet tensor with predicted relational components
which omits the calculations for unpredicted relation types. According to
effective decomposition methods, we propose the Tensor Decomposition based
Relation Extraction (TDRE) approach which is able to extract overlapping
triplets and avoid detecting unnecessary entity pairs. Experiments on benchmark
datasets NYT, CoNLL04 and ADE datasets demonstrate that the proposed method
outperforms existing strong baselines.
