  Transforming a sentence into a two-dimensional (2D) representation (e.g., the
table filling) has the ability to unfold a semantic plane, where an element of
the plane is a word-pair representation of a sentence which may denote a
possible relation representation composed of two named entities. The 2D
representation is effective in resolving overlapped relation instances.
However, in related works, the representation is directly transformed from a
raw input. It is weak to utilize prior knowledge, which is important to support
the relation extraction task. In this paper, we propose a two-dimensional
feature engineering method in the 2D sentence representation for relation
extraction. Our proposed method is evaluated on three public datasets (ACE05
Chinese, ACE05 English, and SanWen) and achieves the state-of-the-art
performance. The results indicate that two-dimensional feature engineering can
take advantage of a two-dimensional sentence representation and make full use
of prior knowledge in traditional feature engineering. Our code is publicly
available at
https://github.com/Wang-ck123/A-Two-Dimensional-Feature-Engineering-Method-for-Entity-Relation-Extraction
