Best ways for Phrase Detection in Python NLP libraries

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Phrase Detection using Spacy –

Spacy is one of the most popular NLP libraries. There are numerous complex operations in NLP that are just a few line games in the Spacy NLP library. For example Name Entity Recognizer, POS tagger, etc. Well! Let’s see how can we detect noun phrases using spacy NLP library-

Step1 –

We need to install Spacy and then you need to import the corresponding model for your language. It will be more clear with the below example.

!pip install spacy
!python -m spacy download en_core_web_sm
import spacy
spacy_obj = spacy.load('en_core_web_sm')

We are running this code on Google collab, That’s why we have used the operator “! ” before “pip install spacy”. This is collab notebook syntax for running code in the command line. If you are running it on PC, you do not need to use it.

Step 2 –

Here we need to parse the text and get the corresponding phrases.

text = ('Data Science Learner is the best website to learn Technology like AI/ML')
spacy_text = spacy_obj(text)
for chunk in spacy_text.noun_chunks:
  print (chunk)

Here the all the extracted phrases are noun only. Spacy has this limitation. Spacy only captures the noun phrases. For Verb Phrases, there is a different module textacy.

Conclusion –

I hope you must like this article – Best ways for Phrase Detection in Python NLP libraries. Most of us also say its keyword extraction. This is really important to understand the important topics in the document. Actually there are so many names for this like concept extraction. Please subscribe to us for more updates on the same topic as we keep updating the article. Please also provide your feedback, That will improve us.

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Data Science Learner Team