Open divyanshukatiyar opened 1 year ago
The module uses simple regex to extract sentences. It would be better to use NLTK for this.
For example like this:
import nltk
sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
sent_detector.tokenize(text.strip())
Otherwise, sentences like "This is Dr. Smith and he lives in London." would get chopped up.
I found a small issue here: →refinery: change ATTRIBUTE = “text” →localhost: Error: Unprocessable Entity
Description This module removes all the irrelevant text from a passage or chat, and return the possible relevant information.
Implementation
Additional context Add any other context or screenshots about the feature request here.