I've previously (in other lemmatization software) seen "inner" -> "inner" (but again, not sure what correct lemma is, only that "inn" seems wrong here).
I did notice some tasks for improving lemmatization in Spacy in future though, so can hold off on reporting lemmatization issues if it's not helpful at the moment.
Example case:
import spacy
nlp = spacy.load('en')
for token in nlp("Wearing inners in the innermost inner space in the inn."):
print(token, token.lemma_)
Output:
Wearing wear
inners inner
in in
the the
innermost innermost
inner inn
space space
in in
the the
inn inn
. .
Info about spaCy
- spaCy version: 1.7.2
- Platform: Darwin-16.4.0-x86_64-i386-64bit
- Python version: 3.6.0
- Installed models: en
I've previously (in other lemmatization software) seen "inner" -> "inner" (but again, not sure what correct lemma is, only that "inn" seems wrong here).
I did notice some tasks for improving lemmatization in Spacy in future though, so can hold off on reporting lemmatization issues if it's not helpful at the moment.
Example case:
Output:
Info about spaCy