# GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

arXiv: https://arxiv.org/abs/1907.05611 · HTML: https://mlsanity.com/paper/1907.05611/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 18 of 18)

- [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://mlsanity.com/paper/2303.00293/) (2023)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Towards More Fine-grained and Reliable NLP Performance Prediction](https://mlsanity.com/paper/2102.05486/) (2021)
- [AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER](https://mlsanity.com/paper/2106.02300/) (2021)
- [Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the Wild](https://mlsanity.com/paper/2307.10214/) (2023)
- [Larger-Context Tagging: When and Why Does It Work?](https://mlsanity.com/paper/2104.04434/) (2021)
- [DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling](https://mlsanity.com/paper/2109.08818/) (2021)
- [AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs](https://mlsanity.com/paper/2409.09704/) (2024)
- [ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2305.17951/) (2023)
- [De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models](https://mlsanity.com/paper/2101.00146/) (2021)
- [Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification](https://mlsanity.com/paper/2103.13546/) (2021)
- [Neural Sequence Segmentation as Determining the Leftmost Segments](https://mlsanity.com/paper/2104.07217/) (2021)
- [Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection](https://mlsanity.com/paper/2409.06285/) (2024)
- [Exploiting Global Contextual Information for Document-level Named Entity Recognition](https://mlsanity.com/paper/2106.00887/) (2021)
- [An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information](https://mlsanity.com/paper/2203.10717/) (2022)
- [Research on Intellectual Property Resource Profile and Evolution Law](https://mlsanity.com/paper/2204.06221/) (2022)
- [SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention](https://mlsanity.com/paper/2304.02901/) (2023)
- [A Global Context Mechanism for Sequence Labeling](https://mlsanity.com/paper/2305.19928/) (2023)

## Abstract (arXiv)

The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.We have made the code publicly available at https://github.com/HuiChen24/NER-GRN.