README

Code for Two-stage Identifier: "Parallel Instance Query Network for Named Entity Recognition", accepted at ACL 2022. For details of the model and experiments, please see our paper.

Setup

Requirements

conda create --name acl python=3.8
conda activate acl
pip install -r requirements.txt

Datasets

Nested NER:

ACE04: https://catalog.ldc.upenn.edu/LDC2005T09
ACE05: https://catalog.ldc.upenn.edu/LDC2006T06
KBP17: https://catalog.ldc.upenn.edu/LDC2017D55
GENIA: http://www.geniaproject.org/genia-corpus
NNE: https://github.com/nickyringland/nested_named_entities

Flat NER:

OntoNotes: https://github.com/yhcc/OntoNotes-5.0-NER
CoNLL03: https://data.deepai.org/conll2003.zip
FewNERD: https://ningding97.github.io/fewnerd/
MSRA: https://github.com/ShannonAI/mrc-for-flat-nested-ner/blob/master/ner2mrc/download.md

Data format:

{
    "tokens": ["Others", ",", "though", ",", "are", "novices", "."], 
    "entities": [{"type": "PER", "start": 0, "end": 1}, {"type": "PER", "start": 5, "end": 6}], "relations": [], "org_id": "CNN_IP_20030328.1600.07", 
    "ltokens": ["WOODRUFF", "We", "know", "that", "some", "of", "the", "American", "troops", "now", "fighting", "in", "Iraq", "are", "longtime", "veterans", "of", "warfare", ",", "probably", "not", "most", ",", "but", "some", ".", "Their", "military", "service", "goes", "back", "to", "the", "Vietnam", "era", "."], 
    "rtokens": ["So", "what", "is", "it", "like", "for", "them", "to", "face", "combat", "far", "from", "home", "?", "For", "an", "idea", ",", "here", "is", "CNN", "'s", "Candy", "Crowley", "with", "some", "war", "stories", "."]
}

The ltokens contains the tokens from the previous sentence. And The rtokens contains the tokens from the next sentence.

Due to the license, we cannot directly release our preprocessed datasets of ACE04, ACE05, KBP17, NNE and OntoNotes. We only release the preprocessed GENIA, FewNERD, MSRA and CoNLL03 datasets. Download them from here.

If you need other datasets, please contact me ([email protected]) by email. Note that you need to state your identity and prove that you have obtained the license.

Example

Train

python piqn.py train --config configs/nested.conf

Note: You should edit this line in config_reader.py according to the actual number of GPUs.

Evaluation

You can download our checkpoints on ACE04 and ACE05, or train your own model and then evaluate the model. Because of the limited space of Google Cloud Drive, we share the other models in Baidu Cloud Drive, please download at this link (code: js9z).

python identifier.py eval --config configs/batch_eval.conf

If you use the checkpoints (ACE05 and ACE04) we provided, you will get the following results:

ACE05:

2022-03-30 12:56:52,447 [MainThread  ] [INFO ]  --- NER ---
2022-03-30 12:56:52,447 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   PER        88.07        92.92        90.43         1724
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   LOC        63.93        73.58        68.42           53
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   WEA        86.27        88.00        87.13           50
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   GPE        87.22        87.65        87.44          405
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   ORG        85.74        81.64        83.64          523
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   VEH        83.87        77.23        80.41          101
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                   FAC        75.54        77.21        76.36          136
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                 micro        86.38        88.57        87.46         2992
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]                 macro        81.52        82.61        81.98         2992
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]  --- NER on Localization ---
2022-03-30 12:56:52,475 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]                Entity        90.58        92.91        91.73         2991
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]                 micro        90.58        92.91        91.73         2991
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]                 macro        90.58        92.91        91.73         2991
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]  --- NER on Classification ---
2022-03-30 12:56:52,496 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   PER        97.09        92.92        94.96         1724
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   LOC        76.47        73.58        75.00           53
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   WEA        95.65        88.00        91.67           50
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   GPE        92.93        87.65        90.22          405
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   ORG        93.85        81.64        87.32          523
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   VEH       100.00        77.23        87.15          101
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                   FAC        89.74        77.21        83.00          136
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]  
2022-03-30 12:56:52,516 [MainThread  ] [INFO ]                 micro        95.36        88.57        91.84         2992
2022-03-30 12:56:52,517 [MainThread  ] [INFO ]                 macro        92.25        82.61        87.05         2992

ACE04

2021-11-15 22:06:50,896 [MainThread  ] [INFO ]  --- NER ---
2021-11-15 22:06:50,896 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   VEH        88.89        94.12        91.43           17
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   WEA        74.07        62.50        67.80           32
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   GPE        89.11        87.62        88.36          719
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   ORG        85.06        84.60        84.83          552
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   FAC        83.15        66.07        73.63          112
2021-11-15 22:06:50,932 [MainThread  ] [INFO ]                   PER        91.09        92.12        91.60         1498
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]                   LOC        72.90        74.29        73.58          105
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]                 micro        88.48        87.81        88.14         3035
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]                 macro        83.47        80.19        81.61         3035
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]  --- NER on Localization ---
2021-11-15 22:06:50,933 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]                Entity        92.56        91.89        92.23         3034
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]                 micro        92.56        91.89        92.23         3034
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]                 macro        92.56        91.89        92.23         3034
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,954 [MainThread  ] [INFO ]  --- NER on Classification ---
2021-11-15 22:06:50,955 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                  type    precision       recall     f1-score      support
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   VEH        94.12        94.12        94.12           17
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   WEA        95.24        62.50        75.47           32
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   GPE        95.60        87.62        91.44          719
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   ORG        93.59        84.60        88.87          552
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   FAC        93.67        66.07        77.49          112
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   PER        97.11        92.12        94.55         1498
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                   LOC        84.78        74.29        79.19          105
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]  
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                 micro        95.59        87.81        91.53         3035
2021-11-15 22:06:50,976 [MainThread  ] [INFO ]                 macro        93.44        80.19        85.87         3035

Citation

If you have any questions related to the code or the paper, feel free to email [email protected].

@inproceedings{shen-etal-2022-piqn,
    title = "Parallel Instance Query Network for Named Entity Recognition",
    author = "Shen, Yongliang  and
      Wang, Xiaobin  and
      Tan, Zeqi  and
      Xu, Guangwei  and
      Xie, Pengjun  and
      Huang, Fei and
      Lu, Weiming and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics",
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2203.10545",
}

Code for "Parallel Instance Query Network for Named Entity Recognition", accepted at ACL 2022.

Related tags

Overview

README

Setup

Requirements

Datasets

Example

Train

Evaluation

Citation

Owner

Yongliang Shen

Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Expressions.

This repository contains the codes for LipGAN. LipGAN was published as a part of the paper titled "Towards Automatic Face-to-Face Translation".

Deep Learning Topics with Computer Vision & NLP

jiant is an NLP toolkit

RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network trained to work with different pairs (images, texts).

Code for ACL 2021 main conference paper "Conversations are not Flat: Modeling the Intrinsic Information Flow between Dialogue Utterances".

OpenChat: Opensource chatting framework for generative models

Code for our paper "Transfer Learning for Sequence Generation: from Single-source to Multi-source" in ACL 2021.

Simple tool/toolkit for evaluating NLG (Natural Language Generation) offering various automated metrics.

Chinese segmentation library

Study German declensions (dER nettE Mann, ein nettER Mann, mit dEM nettEN Mann, ohne dEN nettEN Mann ...) Generate as many exercises as you want using the incredible power of SPACY!

Unofficial PyTorch implementation of Google AI's VoiceFilter system

Code for CodeT5: a new code-aware pre-trained encoder-decoder model.

NLP-based analysis of poor Chinese movie reviews on Douban

My implementation of Safaricom Machine Learning Codility test. The code has bugs, logical I guess I made errors and any correction will be appreciated.

Tevatron is a simple and efficient toolkit for training and running dense retrievers with deep language models.

HAN2HAN : Hangul Font Generation

Python package for performing Entity and Text Matching using Deep Learning.

This repository structures data in title, summary, tags, sentiment given a fragment of a conversation

A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python