A Two Dimensional Feature Engineering Method for Relation Extraction
This work addresses relation extraction for natural language processing, but it is incremental as it builds on existing 2D representation methods by adding feature engineering.
The paper tackles the problem of overlapped relation instances in relation extraction by proposing a two-dimensional feature engineering method that incorporates prior knowledge into 2D sentence representations, achieving state-of-the-art performance on three public datasets (ACE05 Chinese, ACE05 English, and SanWen).
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