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From Andrzej Bialecki>
Subject Re: Stemmer Benefits/Costs
Date Thu, 22 Apr 2004 21:37:42 GMT
Terry Steichen wrote:

> I've been experimenting with the Porter and Snowball stemmers.  It
> seems to me that one of the most valuable benefits these provide is
> the capability to generalize phrase terms.  As a very simple example,
> without the stemmer, I might need to include three phrase terms in my
> query: "north korea", "north korean", "north koreans".  But with the
> stemmer only one will suffice.  To me, that's a huge advantage.  (For
> non-phrases, the advantage doesn't seem to be so great, because much
> the same effect can be achieved with wildcards.)

That's because you look at it from the perspective of English language 
with its minimal inflection... My mother tongue is Polish - a highly 
inflectional language from the Slavic family of languages. It is normal 
for a single Polish word to have as many as 20+ different inflected 
forms (plural/singular/dual, tense, gender, mood, case, infinitive... 
enough? ;-) ). For this type of language studies show that stemming (or 
rather lemmatization - bringing words to their base grammatical forms) 
significantly improves recall in IR systems.

> But there seems to be a price that you also pay, in that
> discrimination may be adversely affected.  If you want to
> discriminate between two terms that the stemmer views as derived from
> the same root, you're out of luck (I think).  The problem with this

Stemming usually improves recall, but lowers precision. For some systems 
it is more desirable to provide any results, even if they are not quite 
correct, than to provide none.

> is that you may start with a set of terms that don't have this
> problem, but over time as new content is added to the index, such
> problems may gradually get introduced - often unpredictably.  And to
> the best of my (admittedly limited) knowledge, once you've indexed
> using a stemmer, there's no way to override it in specific instances.

You can always store in your index stemmed/non-stemmed terms alongside.

> Appreciate any comments, thoughts on the above.

For highly-inflectional languages I had _very_ good results with 
stemmers built using the code from Egothor project 
( - much more sophisticated than simple 
rule-based stemmers like Snowball or Porter. In fact, after proper 
training on a large corpus I was getting ~70% of correct lemmas for 
previously unseen words, and over 90% of correct (unique) stems.

Best regards,
Andrzej Bialecki

Software Architect, System Integration Specialist
CEN/ISSS EC Workshop, ECIMF project chair
EU FP6 E-Commerce Expert/Evaluator
FreeBSD developer (

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