# CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises

arXiv: https://arxiv.org/abs/2210.10581 · HTML: https://mlsanity.com/paper/2210.10581/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [A Hierarchical Framework for Relation Extraction with Reinforcement Learning](https://mlsanity.com/paper/1811.03925/) (2018)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [Graph Neural Networks with Generated Parameters for Relation Extraction](https://mlsanity.com/paper/1902.00756/) (2019)
- [End-to-end neural relation extraction using deep biaffine attention](https://mlsanity.com/paper/1812.11275/) (2018)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing](https://mlsanity.com/paper/1901.08163/) (2019)
- [A Walk-based Model on Entity Graphs for Relation Extraction](https://mlsanity.com/paper/1902.07023/) (2019)
- [BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction](https://mlsanity.com/paper/1908.05908/) (2019)
- [Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification](https://mlsanity.com/paper/1707.07719/) (2017)

## Built on by (most cited first, 1 of 1)

- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)

## Abstract (arXiv)

Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security. However, previous work mainly focuses on getting attribute information about enterprises like personnel and corporate business, and pays little attention to enterprise relation extraction. To encourage further progress in the research, we introduce the CEntRE, a new dataset constructed from publicly available business news data with careful human annotation and intelligent data processing. Extensive experiments on CEntRE with six excellent models demonstrate the challenges of our proposed dataset.