# A Two Dimensional Feature Engineering Method for Relation Extraction

arXiv: https://arxiv.org/abs/2404.04959 · HTML: https://mlsanity.com/paper/2404.04959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 30 of 33)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [LLMaAA: Making Large Language Models as Active Annotators](https://mlsanity.com/paper/2310.19596/) (2023)
- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2112.12731/) (2021)
- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [Autoregressive Structured Prediction with Language Models](https://mlsanity.com/paper/2210.14698/) (2022)
- [Retrieval-Augmented Code Generation for Universal Information Extraction](https://mlsanity.com/paper/2311.02962/) (2023)
- [Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://mlsanity.com/paper/2203.10900/) (2022)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [Relation Classification with Entity Type Restriction](https://mlsanity.com/paper/2105.08393/) (2021)
- [Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion](https://mlsanity.com/paper/2106.08657/) (2021)
- [CodeKGC: Code Language Model for Generative Knowledge Graph Construction](https://mlsanity.com/paper/2304.09048/) (2023)
- [GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling](https://mlsanity.com/paper/1906.02437/) (2019)
- [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022)
- [Rethinking Perturbations in Encoder-Decoders for Fast Training](https://mlsanity.com/paper/2104.01853/) (2021)
- [A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text](https://mlsanity.com/paper/1711.07010/) (2017)
- [Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction](https://mlsanity.com/paper/2212.14270/) (2022)

## Abstract (arXiv)

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