Wordnet is a lexical database created at Princeton University. Its size and several properties it holds make Wordnet one of the most useful tools you can have in your NLP arsenal.
Here are a few properties that make Wordnet so useful:
It’s common in the world on Natural Language Processing to need to compute sentence similarity. Wordnet is an awesome tool and you should always keep it in mind when working with text. It’s of great help for the task we’re trying to tackle.
Suppose we have these sentences:
Part-Of-Speech tagging (or POS tagging, for short) is one of the main components of almost any NLP analysis. The task of POS-tagging simply implies labelling words with their appropriate Part-Of-Speech (Noun, Verb, Adjective, Adverb, Pronoun, …).
Text classification is most probably, the most encountered Natural Language Processing task. It can be described as assigning texts to an appropriate bucket. A sports article should go in
SPORT_NEWS, and a medical prescription should go in
To train a text classifier, we need some annotated data. This training data can be obtained through several methods. Suppose you want to build a spam classifier. You would export the contents of your mailbox. You’d label the email in the inbox folder as
NOT_SPAM and the contents of your spam folder as
In this example I want to show how to use some of the tools packed in NLTK to build something pretty awesome. Inverted indexes are a very powerful tool and is one of the building blocks of modern day search engines.
While building the inverted index, you’ll learn to:
1. Use a stemmer from NLTK
2. Filter words using a stopwords list
3. Tokenize text
You might have stumbled in your NLP application development upon situations when you needed to get the “closest” adjective to a noun, or maybe you needed to “nounify” a verb. After poking around Wordnet I found a simple and pretty effective way to do this. Keep in mind that it is not error proof, but for most of my needs, I found it to perform pretty well. We’ll be using NLTK Wordnet wrapper for this. Let’s have a look at the code:
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