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Interesting to see the Porter2 algorithm (http://snowballstem.org/algorithms/english/stemmer.html http://snowballstem.org/algorithms/english/stemmer.html), havi
by Isamu 10y ago
Interesting to see the Porter2 algorithm (http://snowballstem.org/algorithms/english/stemmer.html http://snowballstem.org/algorithms/english/stemmer.html), having also implemented a Porter stemmer ages ago myself.
But I have to ask, why are we still using this approach, which has known inaccuracies and quirks (in English)? Its one virtue is compactness, which in the old days was more important than accuracy.
But it is fun, I'll grant you that.
- ch4s3 10y agoI think porter2 works well enough. You don't need lexical category like lemmatisation, so it's a lot simpler than that approach.You also don't need a huge volume oftraining data.
- rspeer 10y agoYou don't need any training data to do better than Porter2, just lexical data. If you're content to map the same surface form to the same stem regardless of context, as Porter2 would, what you need is a dictionary mapping words to their stems. This used to be considered a lot of memory to use. It's not anymore. This leaves you with some edge cases (is "axes" the plural of "axis" or "axe"?), but it's much better than mixing up the words "universe" and "university".
- ch4s3 10y agoThat's a good point, I think I overstated the need for training data.