Comments (1)
Hi @Jcerwin,
Sorry for the very late response. I reproduced your issue with the attached file and got the same behaviour with TopicRank:
keyphrases = extractor.get_n_best(n=3)
for keyphrase, score in keyphrases:
print(keyphrase, len(extractor.candidates[keyphrase].sentence_ids), score)
# keyphrase, sentence count, weight
> rude 88 0.026956898839696108
> awful food 0 0.017768482784197532
> service 0 0.01704792703515902
This behaviour is due to the fact that these models rank the normalized forms (stems or lemmas) of the candidates instead of the surface forms, therefore the keys of the extractor.candidates
container are the normalized forms. You can get the sentence counts by setting the stemming
parameter to True
in the get_n_best()
method:
keyphrases = extractor.get_n_best(n=3, stemming=True)
for keyphrase, score in keyphrases:
print(keyphrase, len(extractor.candidates[keyphrase].sentence_ids), score)
> rude 88 0.026956898839696108
> aw food 1 0.017768482784197532
> servic 24 0.01704792703515902
f.
from pke.
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