PURE vs OpenNRE

PURE

[NAACL 2021] A Frustratingly Easy Approach for Entity and Relation Extraction https://arxiv.org/abs/2010.12812 (by princeton-nlp)

NLP relation-extraction

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OpenNRE

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

relation-extraction

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PURE		OpenNRE
	Project
1	Mentions	3
763	Stars	4,246
1.7%	Growth	0.6%
0.0	Activity	6.5
almost 2 years ago	Latest Commit	4 months ago
Python	Language	Python
MIT License	License	MIT License

The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

PURE

Posts with mentions or reviews of PURE. We have used some of these posts to build our list of alternatives and similar projects.

Relationship extraction
1 project | /r/LanguageTechnology | 9 Jun 2021

Searching for good models is actually a review and I encourage you on doing that. Simply search on some papers databases using the desired keywords. One fine model that has been released recently is the PURE model (https://github.com/princeton-nlp/PURE) which solves both Entity and Relation extraction using BERT and achieves SoTA results on ACE.

OpenNRE

Posts with mentions or reviews of OpenNRE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-17.

RAG Using Unstructured Data and Role of Knowledge Graphs
4 projects | news.ycombinator.com | 17 Jan 2024

OpenNRE (https://github.com/thunlp/OpenNRE) is another good approach to neural relation extraction, though it's slightly dated. What would be particularly interesting is to combine models like OpenNRE or SpanMarker with entity-linking models to construct KG triples. And a solid, scalable graph database underneath would make for a great knowledge base that can be constructed from unstructured text.
How to get knowledge graph triples from text?
1 project | /r/MLQuestions | 21 Sep 2022

OpenNRE
KELM: Integrating Knowledge Graphs with Language Model Pre-Training Corpora
5 projects | news.ycombinator.com | 21 May 2021

What are some alternatives?

When comparing PURE and OpenNRE you can also consider the following projects:

bert - TensorFlow code and pre-trained models for BERT

OpenIE-standalone

flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)

CogCompNLP - 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.

rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

minie - An open information extraction system that provides compact extractions

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

DeepKE - [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction

knowledge-net - KnowledgeNet: A Benchmark Dataset for Knowledge Base Population