A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021

Overview

PFN (Partition Filter Network)

This repository contains codes of the official implementation for the paper A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021 [PDF] [PPT]

Quick links

Model Overview

In this work, we present a new framework equipped with a novel recurrent encoder named partition filter encoder designed for multi-task learning. The encoder enforces bilateral interaction between NER and RE in two ways:

  1. The shared partition represents inter-task information and is equally accessible to both tasks, allowing for balanced interaction between NER and RE.
  2. The task partitions represent intra-task information and are formed through concerted efforts of entity and relation gates, making sure that encoding process of entity and relation features are dependent upon each other.

Preparation

Environment Setup

The experiments were performed using one single NVIDIA-RTX3090 GPU. The dependency packages can be installed with the following command:

pip install -r requirements.txt

Also, make sure that the python version is 3.7.10

Data Acquisition and Preprocessing

This is the first work that covers all the mainstream English datasets for evaluation, including [NYT, WEBNLG, ADE, ACE2005, ACE2004, SCIERC, CONLL04]. Please follow the instructions of reademe.md in each dataset folder in ./data/ for data acquisition and preprocessing.

Custom Dataset

If your custom dataset has a large number of triples that contain head-overlap entities (common in Chinese dataset), accuracy of the orignal PFN will not be good.

The orignal one will not be able to decode triples with head-overlap entities. For example, if New York and New York City are both entities, and there exists a RE prediction such as (new, cityof, USA), we cannot know what New corresponds to.

Luckily, the impact on evaluation of English dataset is limited, since such triple is either filtered out (for ADE) or rare (one in test set of SciERC, one in ACE04, zero in other datasets).

You can use our updated PFN-nested to handle the issue. PFN-nested is an enhanced version of PFN. This model is better in leveraging entity tail information and capable of handling nested triple prediction. For usage, replace the files in the root directory with the files in the PFN-nested folder, then follow the directions in Quick Start.

Performance comparison in SciERC

Model NER RE
PFN 66.8 38.4
PFN-nested 67.9 38.7

Quick Start

Model Training

The training command-line is listed below (command for CONLL04 is in Evaluation on CoNLL04):

python main.py \
--data ${NYT/WEBNLG/ADE/ACE2005/ACE2004/SCIERC} \
--do_train \
--do_eval \
--embed_mode ${bert_cased/albert/scibert} \
--batch_size ${20 (for most datasets) /4 (for SCIERC)} \
--lr ${0.00002 (for most datasets) /0.00001 (for SCIERC)} \
--output_file ${the name of your output files, e.g. ace_test} \
--eval_metric ${micro/macro} 

After training, you will obtain three files in the ./save/${output_file}/ directory:

  • ${output_file}.log records the logging information.
  • ${output_file}.txt records loss, NER and RE results of dev set and test set for each epoch.
  • ${output_file}.pt is the saved model with best average F1 results of NER and RE in the dev set.

Evaluation on Pre-trained Model

The evaluation command-line is listed as follows:

python eval.py \
--data ${NYT/WEBNLG/ADE/ACE2005/ACE2004/SCIERC} \
--eval_metric ${micro/macro} \
--model_file ${the path of saved model you want to evaluate. e.g. save/ace_test.pt} \
--embed_mode ${bert_cased/albert/scibert}

Inference on Customized Input

If you want to evaluate the model with customized input, please run the following code:

python inference.py \
--model_file ${the path of your saved model} \
--sent ${sentence you want to evaluate, str type restricted}

{model_file} must contain information about the datasets the model trained on (web/nyt/ade/ace/sci) and the type of pretrained embedding the model uses (albert/bert/scibert). For example, model_file could be set as "web_bert.pt"

Example

input:
python inference.py \
--model_file save/sci_test_scibert.pt \
--sent "In this work , we present a new framework equipped with a novel recurrent encoder   
        named partition filter encoder designed for multi-task learning ."

result:
entity_name: framework, entity type: Generic
entity_name: recurrent encoder, entity type: Method
entity_name: partition filter encoder, entity type: Method
entity_name: multi-task learning, entity type: Task
triple: recurrent encoder, Used-for, framework
triple: recurrent encoder, Part-of, framework
triple: recurrent encoder, Used-for, multi-task learning
triple: partition filter encoder, Hyponym-of, recurrent encoder
triple: partition filter encoder, Used-for, multi-task learning



input:  
python inference.py \
--model_file save/ace_test_albert.pt \
--sent "As Williams was struggling to gain production and an audience for his work in the late 1930s ,  
        he worked at a string of menial jobs that included a stint as caretaker on a chicken ranch in   
        Laguna Beach , California . In 1939 , with the help of his agent Audrey Wood , Williams was 
        awarded a $1,000 grant from the Rockefeller Foundation in recognition of his play Battle of 
        Angels . It was produced in Boston in 1940 and was poorly received ."

result:
entity_name: Williams, entity type: PER
entity_name: audience, entity type: PER
entity_name: his, entity type: PER
entity_name: he, entity type: PER
entity_name: caretaker, entity type: PER
entity_name: ranch, entity type: FAC
entity_name: Laguna Beach, entity type: GPE
entity_name: California, entity type: GPE
entity_name: his, entity type: PER
entity_name: agent, entity type: PER
entity_name: Audrey Wood, entity type: PER
entity_name: Williams, entity type: PER
entity_name: Rockefeller Foundation, entity type: ORG
entity_name: his, entity type: PER
entity_name: Boston, entity type: GPE
triple: caretaker, PHYS, ranch
triple: ranch, PART-WHOLE, Laguna Beach
triple: Laguna Beach, PART-WHOLE, California

Evaluation on CoNLL04

We also run the test on the dataset CoNLL04, but we did not report the results in our paper due to several reasons:

The command for running CoNLL04 is listed below:

python main.py \
--data CONLL04 \
--do_train \
--do_eval \
--embed_mode albert \
--batch_size 10 \
--lr 0.00002 \
--output_file ${the name of your output files} \
--eval_metric micro \
--clip 1.0 \
--epoch 200

Pre-trained Models and Training Logs

We provide you with pre-trained models for NYT/WEBNLG/ACE2005/ACE2004/SCIERC/CONLL04, along with recorded results of each epoch, identical with training results under the specified configurations above.

Download Links

Due to limited space in google drive, 10-fold model files for ADE are not available to you (training record still available).

After downloading the linked files below, unzip them and put ${data}_test.pt in the directory of ./save/ before running eval.py. Also, ${data}_test.txt and ${data}_test.log records the results of each epoch. You should check that out as well.

Dataset File Size Embedding Download
NYT 393MB Bert-base-cased Link
WebNLG 393MB Bert-base-cased Link
ACE05 815MB Albert-xxlarge-v1 Link
ACE04 3.98GB Albert-xxlarge-v1 Link
SciERC 399MB Scibert-uncased Link
ADE 214KB Bert + Albert Link
CoNLL04 815MB Albert-xxlarge-v1 Link

Result Display

F1 results on NYT/WebNLG/ACE05/SciERC:

Dataset Embedding NER RE
NYT Bert-base-cased 95.8 92.4
WebNLG Bert-base-cased 98.0 93.6
ACE05 Albert-xxlarge-v1 89.0 66.8
SciERC Scibert-uncased 66.8 38.4

F1 results on ACE04:

5-fold 0 1 2 3 4 Average
Albert-NER 89.7 89.9 89.5 89.7 87.6 89.3
Albert-RE 65.5 61.4 63.4 61.5 60.7 62.5

F1 results on CoNLL04:

Model Embedding Micro-NER Micro-RE
Table-sequence Albert-xxlarge-v1 90.1 73.6
PFN Albert-xxlarge-v1 89.6 75.0

F1 results on ADE:

10-fold 0 1 2 3 4 5 6 7 8 9 Average
Bert-NER 89.6 92.3 90.3 88.9 88.8 90.2 90.1 88.5 88.0 88.9 89.6
Bert-RE 80.5 85.8 79.9 79.4 79.3 80.5 80.0 78.1 76.2 79.8 80.0
Albert-NER 91.4 92.9 91.9 91.5 90.7 91.6 91.9 89.9 90.6 90.7 91.3
Albert-RE 83.9 86.8 82.8 83.2 82.2 82.4 84.5 82.3 81.9 82.2 83.2

Robustness Against Input Perturbation

We use robustness test to evaluate our model under adverse circumstances. In this case, we use the domain transformation methods of NER from Textflint.

The test files can be found in the folder of ./robustness_data/. Our reported results are evaluated with the linked ACE2005-albert model above. For each test file, move it to ./data/ACE2005/ and rename it as test_triples.json, then run eval.py with the instructions above.

Citation

Please cite our paper if it's helpful to you in your research.

@misc{yan2021partition,
      title={A Partition Filter Network for Joint Entity and Relation Extraction}, 
      author={Zhiheng Yan and Chong Zhang and Jinlan Fu and Qi Zhang and Zhongyu Wei},
      year={2021},
      eprint={2108.12202},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Owner
zhy
Knowledge Graph, Information Extraction, Interpretability of NLP System
zhy
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model

UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model Official repository for the ICCV 2021 paper: UltraPose: Syn

MomoAILab 92 Dec 21, 2022
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection Implementation of the Uniform DL Representation for AD algorithm describ

Paul Irofti 1 Nov 23, 2022
Source code for the ACL-IJCNLP 2021 paper entitled "T-DNA: Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation" by Shizhe Diao et al.

T-DNA Source code for the ACL-IJCNLP 2021 paper entitled Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adapta

shizhediao 17 Dec 22, 2022
Código de um painel de auto atendimento feito em Python.

Painel de Auto-Atendimento O intuito desse projeto era fazer em Python um programa que simulasse um painel de auto atendimento, no maior estilo Mac Do

Calebe Alves Evangelista 2 Nov 09, 2022
A library for building and serving multi-node distributed faiss indices.

About Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don't fit into a single server memory. It fol

Meta Research 170 Dec 30, 2022
Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

RTM3D-PyTorch The PyTorch Implementation of the paper: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving (ECCV 2020

Nguyen Mau Dzung 271 Nov 29, 2022
ISNAS-DIP: Image Specific Neural Architecture Search for Deep Image Prior [CVPR 2022]

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior (CVPR 2022) Metin Ersin Arican*, Ozgur Kara*, Gustav Bredell, Ender Konukogl

Özgür Kara 24 Dec 18, 2022
🛠️ SLAMcore SLAM Utilities

slamcore_utils Description This repo contains the slamcore-setup-dataset script. It can be used for installing a sample dataset for offline testing an

SLAMcore 7 Aug 04, 2022
This is the repository of our article published on MDPI Entropy "Feature Selection for Recommender Systems with Quantum Computing".

Collaborative-driven Quantum Feature Selection This repository was developed by Riccardo Nembrini, PhD student at Politecnico di Milano. See the websi

Quantum Computing Lab @ Politecnico di Milano 10 Apr 21, 2022
PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

Princeton Natural Language Processing 657 Jan 09, 2023
Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

Geometry Processing with Neural Fields Pytorch implementation for the NeurIPS 2021 paper: Geometry Processing with Neural Fields Guandao Yang, Serge B

Guandao Yang 162 Dec 16, 2022
Procedural 3D data generation pipeline for architecture

Synthetic Dataset Generator Authors: Stanislava Fedorova Alberto Tono Meher Shashwat Nigam Jiayao Zhang Amirhossein Ahmadnia Cecilia bolognesi Dominik

Computational Design Institute 49 Nov 25, 2022
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
An AFL implementation with UnTracer (our coverage-guided tracer)

UnTracer-AFL This repository contains an implementation of our prototype coverage-guided tracing framework UnTracer in the popular coverage-guided fuz

113 Dec 17, 2022
[NeurIPS 2021] Low-Rank Subspaces in GANs

Low-Rank Subspaces in GANs Figure: Image editing results using LowRankGAN on StyleGAN2 (first three columns) and BigGAN (last column). Low-Rank Subspa

112 Dec 28, 2022
Semantic similarity computation with different state-of-the-art metrics

Semantic similarity computation with different state-of-the-art metrics Description • Installation • Usage • License Description TaxoSS is a semantic

6 Jun 22, 2022
Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

FNet: Mixing Tokens with Fourier Transforms Pytorch implementation of Fnet : Mixing Tokens with Fourier Transforms. Citation: @misc{leethorp2021fnet,

Rishikesh (ऋषिकेश) 218 Jan 05, 2023
Software associated to AAAI paper "Planning with Biological Neurons and Synapses"

jBrain Software associated with the AAAI 2022 paper Francesco D'Amore, Daniel Mitropolsky, Pierluigi Crescenzi, Emanuele Natale, Christos H. Papadimit

Pierluigi Crescenzi 1 Apr 10, 2022
Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning

T2I_CL This is the official Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning Requirements Linux Python

42 Dec 31, 2022
FOSS Digital Asset Distribution Platform built on Frappe.

Digistore FOSS Digital Assets Marketplace. Distribute digital assets, like a pro. Video Demo Here Features Create, attach and list digital assets (PDF

Mohammad Hussain Nagaria 30 Dec 08, 2022