Opinions on KStem vs Porter Stem?

I'm thinking of switching from the Porter stemmer to KStem for English
because it seems to do a better job for my rather limited test set. Does
anyone have an opinion on which stemmer seems to do a better job?

Thanks!

Nik

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Hi,

if I remember correctly, both Porter and English Snowball can be quite
aggressive in stemming. We are using KStem. YMMW

Regards,
Lukas

On Tue, Oct 1, 2013 at 2:57 PM, Nikolas Everett nik9000@gmail.com wrote:

I'm thinking of switching from the Porter stemmer to KStem for English
because it seems to do a better job for my rather limited test set. Does
anyone have an opinion on which stemmer seems to do a better job?

Thanks!

Nik

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On Tue, Oct 1, 2013 at 10:07 AM, Lukáš Vlček lukas.vlcek@gmail.com wrote:

if I remember correctly, both Porter and English Snowball can be quite
aggressive in stemming. We are using KStem. YMMW

Thanks!

Nik

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Same here I find kstem less aggressive and a better fit for my data

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Likewise here.
We just switched from Porter to KStem for a recent client. I don't think
we've ever moved in the other direction.

Otis

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On Tuesday, October 1, 2013 5:22:17 PM UTC-4, AlexR wrote:

Same here I find kstem less aggressive and a better fit for my data

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Thanks so much. I did end up switching to KStem. I've only seen one
complaint, around the last name "Duhring", but I'm pretty sure Porter
wouldn't have been any better there.

Nik

On Mon, Oct 7, 2013 at 2:53 PM, Otis Gospodnetic <otis.gospodnetic@gmail.com

wrote:

Likewise here.
We just switched from Porter to KStem for a recent client. I don't think
we've ever moved in the other direction.

Otis

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Search Analytics - http://sematext.com/search-analytics/index.html

On Tuesday, October 1, 2013 5:22:17 PM UTC-4, AlexR wrote:

Same here I find kstem less aggressive and a better fit for my data

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I'll too be moving over to kstem. In addition to being a bit less aggressive, kstem turns all words into other real words (porter often comes out with word-chunks). So you can use the analyzed result to generate word clouds or other types of aggregations.