jiacheng-ye/DocL-NER
Code for our paper "Leveraging Document-Level Label Consistency for Named Entity Recognition" and "Uncertainty-Aware Sequence Labeling"
This project helps natural language processing researchers and practitioners enhance named entity recognition (NER) performance. It takes text documents in CoNLL format and outputs the same documents with improved entity labels, especially by considering consistency across an entire document. This is for researchers and developers working on advanced NLP models for information extraction.
No commits in the last 6 months.
Use this if you are a researcher or NLP engineer looking to implement state-of-the-art named entity recognition models, particularly those that leverage document-level context for improved accuracy.
Not ideal if you need a simple, out-of-the-box named entity recognition tool without deep understanding of model training or sequence labeling techniques.
Stars
19
Forks
3
Language
Python
License
—
Category
Last pushed
May 24, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/jiacheng-ye/DocL-NER"
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