A Two Dimensional Feature Engineering Method for Relation Extraction

TL;DR

This paper introduces a novel 2D feature engineering approach for relation extraction that enhances prior knowledge utilization and achieves state-of-the-art results on multiple datasets.

Contribution

It proposes a 2D feature engineering method that improves relation extraction by effectively leveraging prior knowledge within a 2D sentence representation.

Findings

Achieved state-of-the-art performance on three public datasets.

Effectively utilizes prior knowledge in 2D relation extraction.

Improves handling of overlapped relation instances.

Abstract

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