This is a demonstration of NLTK part of speech taggers
and NLTK chunkers using
These taggers can assign part-of-speech tags to each word in your text.
They can also identify certain phrases/chunks and named entities.
The default part of speech tagger is a classifier based tagger trained on the PENN Treebank corpus. The PENN Treebank corpus is composed of news articles from the reuters newswire. That means the tagger is more likely to be correct on text that looks like a news article, and less accurate on text that doesn't.
The default chunker is a classifier based chunker trained on the ACE corpus. This means it recognizes noun phrases and named entities, such as locations, names, organizations, and more. It will only work well with an English tagger, and will work best with the default tagger.
If you'd like to use this thru an API, please see the API docs for Tagging & Chunking and Phrase Extraction & Named Entity Recognition. And for higher limits and premium API access, signup for the Mashape Text-Processing API.
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