SpanNER: Named EntityRe-/Recognition as Span Prediction

Related tags

Deep LearningSpanNER
Overview

SpanNER: Named EntityRe-/Recognition as Span Prediction

Overview | Demo | Installation | Preprocessing | Prepare Models | Running | System Combination | Bib

This repository contains the code for our paper SpanNER: Named EntityRe-/Recognition as Span Prediction (ACL 2021).

The model designed in this work has been deployed into ExplainaBoard.

Overview

We investigate complementary advantages of systems based on different paradigms: span prediction model and sequence labeling framework. We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems’ outputs. We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners.

d

Demo

We deploy SpanNER into the ExplainaBoard.

Quick Installation

  • python3
  • PyTorch
  • pytorch-lightning

Run the following script to install the dependencies,

pip3 install -r requirements.txt

Data Preprocessing

The dataset needs to be preprocessed, before running the model. We provide dataprocess/bio2spannerformat.py for reference, which gives the CoNLL-2003 as an example. First, you need to download datasets, and then convert them into BIO2 tagging format. We provided the CoNLL-2003 dataset with BIO format in data/conll03_bio folder, and its preprocessed format dataset in data/conll03 folder.

The download links of the datasets used in this work are shown as follows:

Prepare Models

For English Datasets, we use BERT-Large.

For Dutch and Spanish Datasets, we use BERT-Multilingual-Base.

How to Run?

Here, we give CoNLL-2003 as an example. You may need to change the DATA_DIR, PRETRAINED, dataname, n_class to your own dataset path, pre-trained model path, dataset name, and the number of labels in the dataset, respectively.

./run_conll03_spanner.sh

System Combination

Base Model

We provided 12 base models (result-files) of CoNLL-2003 dataset in combination/results. More base model (result-files) can be download from ExplainaBoard-download.

Combination

Put your different base models (result-files) in the data/results folder, then run:

python comb_voting.py

Here, we provided four system combination methods, including:

  • SpanNER,
  • Majority voting (VM),
  • Weighted voting base on overall F1-score (VOF1),
  • Weighted voting base on class F1-score (VCF1).

Results at a Glance

d

Bib

@article{fu2021spanner,
  title={SpanNer: Named Entity Re-/Recognition as Span Prediction},
  author={Fu, Jinlan and Huang, Xuanjing and Liu, Pengfei},
  journal={arXiv preprint arXiv:2106.00641},
  year={2021}
}
Owner
NeuLab
Graham Neubig's Lab at LTI/CMU
NeuLab
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

8 Nov 14, 2022
Implementation of the ICCV'21 paper Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [Papers 1, 2][Project page] [Video] The implementation of the papers Temporal

56 Nov 21, 2022
AFLNet: A Greybox Fuzzer for Network Protocols

AFLNet: A Greybox Fuzzer for Network Protocols AFLNet is a greybox fuzzer for protocol implementations. Unlike existing protocol fuzzers, it takes a m

626 Jan 06, 2023
PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

Yulun Zhang 1.2k Dec 26, 2022
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks This folder contains the code to reproduce the data in "The Implicit Bias o

Samuel Lippl 0 Feb 05, 2022
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
Localization Distillation for Object Detection

Localization Distillation for Object Detection This repo is based on mmDetection. This is the code for our paper: Localization Distillation

274 Dec 26, 2022
Encoding Causal Macrovariables

Encoding Causal Macrovariables Data Natural climate data ('El Nino') Self-generated data ('Simulated') Experiments Detecting macrovariables through th

Benedikt Höltgen 3 Jul 31, 2022
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

Simulated+Unsupervised (S+U) Learning in TensorFlow TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial T

Taehoon Kim 569 Dec 29, 2022
Meta Learning Backpropagation And Improving It (VSML)

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

Louis Kirsch 22 Dec 21, 2022
Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation Created by Zeyu HU Introduction This work is based on our paper VMNet: Voxel-Mes

HU Zeyu 82 Dec 27, 2022
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

Denis 29 Nov 21, 2022
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Agnieszka Mikołajczyk 88 Nov 17, 2022
Yolov3 pytorch implementation

YOLOV3 Pytorch实现 在bubbliiing大佬代码的基础上进行了修改,添加了部分注释。 预训练模型 预训练模型来源于bubbliiing。 链接:https://pan.baidu.com/s/1ncREw6Na9ycZptdxiVMApw 提取码:appk 训练自己的数据集 按照VO

4 Aug 27, 2022
Zsseg.baseline - Zero-Shot Semantic Segmentation

This repo is for our paper A Simple Baseline for Zero-shot Semantic Segmentation

98 Dec 20, 2022
Implementation of Deep Deterministic Policy Gradiet Algorithm in Tensorflow

ddpg-aigym Deep Deterministic Policy Gradient Implementation of Deep Deterministic Policy Gradiet Algorithm (Lillicrap et al.arXiv:1509.02971.) in Ten

Steven Spielberg P 247 Dec 07, 2022
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

#NeuralTalk Warning: Deprecated. Hi there, this code is now quite old and inefficient, and now deprecated. I am leaving it on Github for educational p

Andrej 5.3k Jan 07, 2023
This is the implementation of GGHL (A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection)

GGHL: A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection This is the implementation of GGHL 👋 👋 👋 [Arxiv] [Google Drive][B

551 Dec 31, 2022
HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events globally on daily to subseasonal timescales.

HeatNet HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events glob

Google Research 6 Jul 07, 2022
Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021)

Transferable Semantic Augmentation for Domain Adaptation Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021) Paper

66 Dec 16, 2022