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

Related tags

Text Data & NLPpiqn
Overview

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:

Flat NER:

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",
}
Owner
Yongliang Shen
Knowledge is power.
Yongliang Shen
NLP-based analysis of poor Chinese movie reviews on Douban

douban_embedding 豆瓣中文影评差评分析 1. NLP NLP(Natural Language Processing)是指自然语言处理,他的目的是让计算机可以听懂人话。 下面是我将2万条豆瓣影评训练之后,随意输入一段新影评交给神经网络,最终AI推断出的结果。 "很好,演技不错

3 Apr 15, 2022
:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

Dedupe Python Library dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on

Dedupe.io 3.6k Jan 02, 2023
숭실대학교 컴퓨터학부 전공종합설계프로젝트

✨ 시각장애인을 위한 버스도착 알림 장치 ✨ 👀 개요 현대 사회에서 대중교통 위치 정보를 이용하여 사람들이 간단하게 이용할 대중교통의 정보를 얻고 쉽게 대중교통을 이용할 수 있다. 해당 정보는 각종 어플리케이션과 대중교통 이용시설에서 위치 정보를 제공하고 있지만 시각

taegyun 3 Jan 25, 2022
SummerTime - Text Summarization Toolkit for Non-experts

A library to help users choose appropriate summarization tools based on their specific tasks or needs. Includes models, evaluation metrics, and datasets.

Yale-LILY 213 Jan 04, 2023
Implementation of TF-IDF algorithm to find documents similarity with cosine similarity

NLP learning Trying to learn NLP to use in my projects! Table of Contents About The Project Built With Getting Started Requirements Run Usage License

Faraz Farangizadeh 3 Aug 25, 2022
Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models

Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model.

Prithivida 681 Jan 01, 2023
jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese.

jel: Japanese Entity Linker jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese. Usage Currently, link and question methods

izuna385 10 Jan 06, 2023
Searching keywords in PDF file folders

keyword_searching Steps to use this Python scripts: (1)Paste this script into the file folder containing the PDF files you need to search from; (2)Thi

1 Nov 08, 2021
Deduplication is the task to combine different representations of the same real world entity.

Deduplication is the task to combine different representations of the same real world entity. This package implements deduplication using active learning. Active learning allows for rapid training wi

63 Nov 17, 2022
Turkish Stop Words Türkçe Dolgu Sözcükleri

trstop Turkish Stop Words Türkçe Dolgu Sözcükleri In this repository I put Turkish stop words that is contained in the first 10 thousand words with th

Ahmet Aksoy 103 Nov 12, 2022
Simple GUI where you can enter an article and get a crisp summarized version.

Text-Summarization-using-TextRank-BART Simple GUI where you can enter an article and get a crisp summarized version. How to run: Clone the repo Instal

Rohit P 4 Sep 28, 2022
The simple project to separate mixed voice (2 clean voices) to 2 separate voices.

Speech Separation The simple project to separate mixed voice (2 clean voices) to 2 separate voices. Result Example (Clisk to hear the voices): mix ||

vuthede 31 Oct 30, 2022
BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

OpenBMB 377 Jan 02, 2023
Blackstone is a spaCy model and library for processing long-form, unstructured legal text

Blackstone Blackstone is a spaCy model and library for processing long-form, unstructured legal text. Blackstone is an experimental research project f

ICLR&D 579 Jan 08, 2023
Yet Another Sequence Encoder - Encode sequences to vector of vector in python !

Yase Yet Another Sequence Encoder - encode sequences to vector of vectors in python ! Why Yase ? Yase enable you to encode any sequence which can be r

Pierre PACI 12 Aug 19, 2021
Reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer: Self-Attention with Linear Complexity)

Linear Multihead Attention (Linformer) PyTorch Implementation of reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer:

Kui Xu 58 Dec 23, 2022
基于Transformer的单模型、多尺度的VAE模型

UniVAE 基于Transformer的单模型、多尺度的VAE模型 介绍 https://kexue.fm/archives/8475 依赖 需要大于0.10.6版本的bert4keras(当前还没有推到pypi上,可以直接从GitHub上clone最新版)。 引用 @misc{univae,

苏剑林(Jianlin Su) 49 Aug 24, 2022
A fast and easy implementation of Transformer with PyTorch.

FasySeq FasySeq is a shorthand as a Fast and easy sequential modeling toolkit. It aims to provide a seq2seq model to researchers and developers, which

宁羽 7 Jul 18, 2022
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Phil Wang 5k Jan 02, 2023
Auto_code_complete is a auto word-completetion program which allows you to customize it on your needs

auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is one of the deep-learning NLP(Natural Language Process) model struc

RUO 2 Feb 22, 2022