Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取

Entity and Relation Extraction Based on TensorFlow and BERT. 基于TensorFlow和BERT的管道式实体及关系抽取，2019语言与智能技术竞赛信息抽取任务解决方案。Schema based Knowledge Extraction, SKE 2019

[NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812

Span-based system for named entity, relation, and event extraction.

word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding

Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.

Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (ACL 2024)

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

Implementation of our papers Joint entity recognition and relation extraction as a multi-head selection problem (Expert Syst. Appl, 2018) and Adversarial training for multi-context joint entity and relation extraction (EMNLP, 2018).

Source code for "Packed Levitated Marker for Entity and Relation Extraction"

Code associated with the paper **Two are Better Than One: Joint Entity and Relation Extraction with Table-Sequence Encoders**, at EMNLP 2020

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

Convolution neural network for relation extraction between two given entities

实体关系抽取pipline方式，使用了BiLSTM+CRF+BERT

BERT + reproduce "Joint entity recognition and relation extraction as a multi-head selection problem" for Chinese and English IE

An Evaluation of ChatGPT on Information Extraction task, including Named Entity Recognition (NER), Relation Extraction (RE), Event Extraction (EE) and Aspect-based Sentiment Analysis (ABSA).

This repository implements our ACL Findings 2022 research paper RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction. The goal of Zero-Shot Relation Triplet Extraction (ZeroRTE) is to extract relation triplets of the format (head entity, tail entity, relation), despite not having annotated data for the test relation labels.

实体关系抽取，使用了百度比赛的数据集。使用pytorch实现MultiHeadJointEntityRelationExtraction，包含Bert、Albert、gru的使用，并且添加了对抗训练。最后使用Flask和Neo4j图数据库对模型进行了部署

[NAACL 2022 Findings] Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021. It is based on our NERE toolkit (https://github.com/Receiling/NERE).

Code for modeling encoder-decoder architecture with pointer network for joint entity and relation extraction (AAAI 2020).

从模型训练到部署，实战知识图谱(Knowledge Graph)&自然语言处理(NLP)。涉及 Tensorflow, Bert+Bi-LSTM+CRF,Neo4j等 涵盖 Named Entity Recognition,Text Classify,Information Extraction,Relation Extraction 等任务。

GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction

This repository contains the corpora and supplementary data, along with instructions for recreating the experiments, for our paper: "End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models".

基于依存分析的实体关系抽取简单实现，即抽取事实三元组

Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

Code for ACL 2019 : Entity-Relation Extraction as Multi-Turn Question Answering

Entity linking, entity typing and relation extraction: Matching CSV to a Wikibase instance (e.g., Wikidata) via Meta-lookup

This is code for Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging (AAAI 2023)

The source code of the paper "OneRel: Joint Entity and Relation Extraction with One Module in One Step"