shangjingbo1226/AutoNER
Learning Named Entity Tagger from Domain-Specific Dictionary
AutoNER helps you automatically identify specific types of entities, like drug names or medical conditions, in large collections of text without needing to manually label individual words. You provide raw text documents and existing domain-specific dictionaries, and it outputs a model that can find and classify these entities. This is ideal for researchers, analysts, or anyone working with specialized text data who needs to extract key information quickly.
485 stars. No commits in the last 6 months.
Use this if you need to extract specific terms (named entities) from domain-specific text, and you have dictionaries of those terms but lack the resources to manually annotate text line by line.
Not ideal if you don't have any existing dictionaries for the entities you want to find, or if you require extremely high precision that can only be achieved with extensive manual annotation.
Stars
485
Forks
91
Language
ChucK
License
Apache-2.0
Category
Last pushed
Oct 05, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/shangjingbo1226/AutoNER"
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