Comments (1)
By default, the spaCy extension uses the POS tag assigned to the token – so if your spaCy model predicted NOUN
, it will look up this sense. To use entity labels, you can check the respective attributes on the entrie in doc.ents
instead.
If you want more control over the senses and apply them manually, you might prefer the standalone sense2vec
usage. The pipeline component is really just a wrapper around the library.
from sense2vec.
Related Issues (20)
- sense2vec own model issue HOT 3
- Hundreds of thousands of files when training new vectors
- last file not saved properly in scripts/01-parse.py HOT 1
- ignore_case option for get_best_sense and get_other_senses does not check for lowercase
- error runing 03_glove_build_counts.py HOT 3
- plug sense2vec it into your spaCy pipeline HOT 2
- Is there any way to use "doc.spans" in 01_parse.py?
- cant train sense2vec from scratch HOT 1
- Add keyphrases in custom model
- No Build for m1 Mac HOT 2
- Get most similar senses on multiple senses. HOT 2
- different results on the demo than through the spacy script
- Train sense2vec in Chinese HOT 1
- provide citation HOT 1
- Reproduce https://demos.explosion.ai/sense2vec quality results locally HOT 2
- [Question] Why not Word2Vec? HOT 1
- s2v standalone breaks if require_gpu() is called from spacy (cupy) HOT 2
- how to do vector arithmetic? HOT 4
- import error HOT 4
- Inconsistency between demo and github vectors
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from sense2vec.