Stanford NLP Relation Extraction
Lexical semantic relations
Many NLP applications require understanding relations between word senses: synonymy, antonymy, hyponymy, meronymy.
WordNet is a machine-readable database of relations between word senses, and an indispensable resource in many NLP tasks.
http://wordnetweb.princeton.edu/perl/webwn
But WordNet is manually constructed, and has many gaps!
Relation extraction: 5 easy methods
1. Hand-built patterns
2. Bootstrapping methods
3. Supervised methods
4. Distant supervision
5. Unsupervised methods
Please read full article from Stanford NLP Relation Extraction
Lexical semantic relations
Many NLP applications require understanding relations between word senses: synonymy, antonymy, hyponymy, meronymy.
WordNet is a machine-readable database of relations between word senses, and an indispensable resource in many NLP tasks.
http://wordnetweb.princeton.edu/perl/webwn
But WordNet is manually constructed, and has many gaps!
Relation extraction: 5 easy methods
1. Hand-built patterns
2. Bootstrapping methods
3. Supervised methods
4. Distant supervision
5. Unsupervised methods
Please read full article from Stanford NLP Relation Extraction
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