FAQ: Text Preprocessing - Stemming

This community-built FAQ covers the “Stemming” exercise from the lesson “Text Preprocessing”.

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This exercise can be found in the following Codecademy content:

Natural Language Processing

FAQs on the exercise Stemming

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It’s interesting to point out that the PorterStemmer also performs .lower on every word it will return!

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why do we initialize PorterStemmer() while we don’t do it with other built-in libraries?

hey @vlearnstocode ! Well, the logic is that you are creating a stemmer, it will iterate through your words and stem them;
Since you are creating an instance of it, one reason to do so is that you could have different stemmers in the same piece of code (multiple languages, multiple optional parameters). I think you could simply make a call to PorterStemmer().stem([‘words’, ‘list’]) as well. Haven’t tried it though!

oh! that’s very logical!

thank you, kind soul

Interesting, that PorterStemmer from NLTK is quite far from ideal. For example, it has transformed ‘Pacific’ into ‘pacif’, ‘people’ into ‘peopl’. What is common way to deal with this algorithm behaviour?

I’d consider “people” to “peopl-” to be the correct kind of stemming though Pacific to pacif- (root of e.g. pacify) is a sign of over-stemming. With this kind of algorithmic approach it won’t be perfect. There are other stemming tools you could use and they may or may not catch more edge cases (with varying computational costs) but you always have some limitations. If you’re curious have a web search for some more details.

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