# CoRec: An Easy Approach for Coordination Recognition

arXiv: https://arxiv.org/abs/2311.18712 · HTML: https://mlsanity.com/paper/2311.18712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples](https://mlsanity.com/paper/1805.06556/) (2018)
- [An Effective Transition-based Model for Discontinuous NER](https://mlsanity.com/paper/2004.13454/) (2020)
- [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://mlsanity.com/paper/2010.10418/) (2020)
- [A Neural Network for Coordination Boundary Prediction](https://mlsanity.com/paper/1610.03946/) (2016)
- [Improving a Strong Neural Parser with Conjunction-Specific Features](https://mlsanity.com/paper/1702.06733/) (2017)

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

- [A fast and sound tagging method for discontinuous named-entity recognition](https://mlsanity.com/paper/2409.16243/) (2024)

## Abstract (arXiv)

In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.