# Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

@article{Su2022GlobalPN,
title={Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition},
author={Jianlin Su and Ahmed Murtadha and Shengfeng Pan and Jing Hou and Jun Sun and Wanwei Huang and Bo Wen and Yunfeng Liu},
journal={ArXiv},
year={2022},
volume={abs/2208.03054}
}
• Published 5 August 2022
• Computer Science
• ArXiv
We extensively evaluate GP on various benchmark datasets. Our extensive experiments demonstrate that GP can outperform the existing solution. Moreover, the experimental results show the efﬁcacy of the introduced loss function compared to softmax and entropy alternatives. experimental results show the efﬁcacy of the introduced loss function compared to softmax and entropy alternatives.

## References

SHOWING 1-10 OF 67 REFERENCES

### Code-Switched Named Entity Recognition with Embedding Attention

• Computer Science
CodeSwitch@ACL
• 2018
We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian named entity recognition and third place

### Towards Improving Neural Named Entity Recognition with Gazetteers

• Computer Science
ACL
• 2019
It is shown that properly utilizing external gazetteers could benefit segmental neural NER models by adding a simple module on the recently proposed hybrid semi-Markov CRF architecture and observing some promising results.

### A Boundary-aware Neural Model for Nested Named Entity Recognition

• Computer Science
EMNLP
• 2019
This work proposes a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model.

### Robust Lexical Features for Improved Neural Network Named-Entity Recognition

• Computer Science
COLING
• 2018
This work proposes to embed words and entity types into a low-dimensional vector space the authors train from annotated data produced by distant supervision thanks to Wikipedia, and compute a feature vector representing each word that establishes a new state-of-the-art F1 score.

### Neural Metric Learning for Fast End-to-End Relation Extraction

• Computer Science
ArXiv
• 2019
A novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, that improves on the state-of-the-art without the need for global optimization is introduced.

### Named entity recognition in query

• Computer Science
SIGIR
• 2009
Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods.

### Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

• Computer Science
ACL
• 2017
A novel tagging scheme is proposed that can convert the joint extraction task to a tagging problem, and different end-to-end models are studied to extract entities and their relations directly, without identifying entities and relations separately.

### Two Are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

• Computer Science
EMNLP
• 2020
It is argued that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process, and proposed is the novel {\em table-sequence encoder} where two different encoder -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.

### Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition

• Computer Science
AAAI
• 2020
This work proposes a boundary enhanced neural span classification model that has the ability to generate high-quality candidate spans and greatly reduces the time complexity during inference, and incorporates an additional boundary detection task to predict those words that are boundaries of entities.

### Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Network

• Computer Science
CCL
• 2017
This paper proposes a novel end-to-end neural model to jointly extract entities and relations in a sentence that uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing tools, such as dependency parser.