#Nltk pos tag list code

Input: text_news = nltk.Text(word.lower() for word in (categories='news')) We are going to use the news category of the corpus. Here we can see that we are having a corpus of 15 categories. For introducing those features, let us just import the brown corpus.

This is not enough there are some more features we can use. Input: sentence = word_tokenize('allow us to add lines in list of allow actions')Īgain we have provided the exact tag to the ‘allow’ word. Here we can see for the first applicant that the label is NN, a noun, and for the second applicant, it labelled the word as JJ, which means adjective. So lets check for the labels it will give to both of them. In the input, we have provided the applicant word two times with different parts of speech. Input: sentence = word_tokenize('applicant is removed from applicant list of the job ') Let’s check for some more examples this time, we are focusing on homonyms. For example, the word “world” has got the tag NN, a noun, and great has got the tag JJ, which is a tag for an adjective. Here we can see that we have provided tags to every word. Input: sentence = word_tokenize('whatever the world is a great place') Let’s check for the tags for any sentence. Here we can see the list or set of the tag which nltk provides us, and from those options, we will provide labels to every word. Nltk.download('averaged_perceptron_tagger')

Code Implementation: Parts Of Speech Tagging Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions.

#Nltk pos tag list code