There are 67 repositories under relation-extraction topic.
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
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
📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).
An elegent pytorch implement of transformers
knowledge graph知识图谱,从零开始构建知识图谱
基于Pytorch和torchtext的自然语言处理深度学习框架。
[NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812
中文实体关系抽取,pytorch,bilstm+attention
The online version is temporarily unavailable because we cannot afford the key. You can clone and run it locally. Note: we set defaul openai key. If keys exceed plan and are invalid, please tell us. The response speed depends on openai. ( sometimes, the official is too crowded and slow)
A curated list of awesome knowledge graph tutorials, projects and communities.
PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper
Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.
LLM-based ontological extraction tools, including SPIRES
A curated list of Open Information Extraction (OIE) resources: papers, code, data, etc.
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.
Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)
REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).
Distantly Supervised Relation Extraction
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)
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
PyTorch implementation of the position-aware attention model for relation extraction