dmis-lab/GeNER
Simple Questions Generate Named Entity Recognition Datasets (EMNLP 2022)
This project helps build Named Entity Recognition (NER) models for specialized topics without needing extensive human-labeled data. You input simple natural language questions about the entity types you want to find (e.g., "Which fighter aircraft?"), and it outputs a dataset ready for training an NER model. This is ideal for data scientists, machine learning engineers, or domain experts looking to extract specific information from text.
No commits in the last 6 months.
Use this if you need to build a Named Entity Recognition model for niche or emerging entity types but lack the resources or time to manually annotate large datasets.
Not ideal if you already have large, high-quality human-annotated datasets for your specific NER task, or if your computational resources are limited.
Stars
76
Forks
8
Language
Python
License
MIT
Category
Last pushed
Apr 10, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/dmis-lab/GeNER"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
hellohaptik/chatbot_ner
chatbot_ner: Named Entity Recognition for chatbots.
openeventdata/mordecai
Full text geoparsing as a Python library
Rostlab/nalaf
NLP framework in python for entity recognition and relationship extraction
mpuig/spacy-lookup
Named Entity Recognition based on dictionaries
NorskRegnesentral/skweak
skweak: A software toolkit for weak supervision applied to NLP tasks