relation-extraction

An Open-Source Package for Neural Relation Extraction (NRE)

农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策

该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记

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

An Open Toolkit for Knowledge Graph Extraction and Construction

A collection of research on knowledge graphs

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

Must-read papers on neural relation extraction (NRE)

📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).

Neural Relation Extraction, including CNN, PCNN, CNN+ATT, PCNN+ATT

基于Pytorch和torchtext的自然语言处理深度学习框架。

A Large-Scale Few-Shot Relation Extraction Dataset

A curated list of awesome knowledge graph tutorials, projects and communities.

中文实体关系抽取，pytorch，bilstm+attention

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. Accepted by ACL 2020.

NAACL'2021: A Frustratingly Easy Approach for Entity and Relation Extraction

PyTorch code for SpERT: Span-based Entity and Relation Transformer

CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.

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

Distantly Supervised Relation Extraction

A curated list of Open Information Extraction (OIE) resources: papers, code, data, etc.

Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

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).

Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (authors' PyTorch implementation)

DoTAT 是一款基于web、面向领域的通用文本标注工具，支持大规模实体标注、关系标注、事件标注、文本分类、基于字典匹配和正则匹配的自动标注以及用于实现归一化的标准名标注，同时也支持迭代标注、嵌套实体标注和嵌套事件标注。标注规范可自定义且同类型任务中可“一次创建多次复用”。通过分级实体集合扩大了实体类型的规模，并设计了全新高效的标注方式，提升了用户体验和标注效率。此外，本工具增加了审核环节，可对多人的标注结果进行一致性检验、自动合并和手动调整，提高了标注结果的准确率。

Multiple-Relations-Extraction-Only-Look-Once. Just look at the sentence once and extract the multiple pairs of entities and their corresponding relations. 端到端联合多关系抽取模型，可用于 http://lic2019.ccf.org.cn/kg 信息抽取。

PyTorch implementation of the position-aware attention model for relation extraction

Macadam是一个以Tensorflow(Keras)和bert4keras为基础，专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、BiRNN、RCNN、DCNN、CRNN、DeepMoji、SelfAttention、HAN、Capsule等文本分类算法; 支持CRF、Bi-LSTM-CRF、CNN-LSTM、DGCNN、Bi-LSTM-LAN、Lattice-LSTM-Batch、MRC等序列标注算法。

LanguageCrunch NLP server docker image

Pytorch implementation of R-BERT: "Enriching Pre-trained Language Model with Entity Information for Relation Classification"