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From Owen Densmore <>
Subject PHP-Lucene Integration
Date Sun, 27 Mar 2005 03:49:54 GMT
Thanks all for the interesting responses.  Sorry for being a bit late 
in responding!

	-- Owen

Owen Densmore - - -

Begin forwarded message:

> From: "Philippe Ombredanne" <>
> Subject: RE: PHP-Lucene Integration
> Owen,
> very interesting!
> Anything (code) you can share?

Hi Philippe.  We will definitely make our code available.  I suspect, 
however, it is not terribly interesting.  But if simply useful as a 
"case study" that would still be good.

> From: Dawid Weiss <>
> Subject: Re: PHP-Lucene Integration
> Your implementation and ideas sound very interesting, Owen. Can we see 
> the system anywhere in public (and play with it?)

We'll send a link to the site fairly soon.  We're having our final 
review tomorrow, and should have a good idea when we can let folks look 
at it.

>> We are hoping the institute can afford to have us work on true 
>> clustering techniques such as Carrot2 uses. (Thanks to Dawid and all 
>> the Poznan University folks who's papers were so stimulating!)
> You are very welcome. We are also academic, so in the feeling of 
> brotherhood we might help you set up a demo on-line clustering server 
> free of charge. There really is not better clustering technique than 
> the one devised to a particular problem and it seems like you found 
> that niche. Although it's always worth experimenting with other stuff 
> just for the sake of comparison. Just let me know if you're interested 
> (if we  can access the 'feed' of those plain search results I can set 
> up the clustering demo in a few minutes, really).

This would be really great!  Indeed, we'd like to help SFI to be a bit 
more involved with exploring their collection with innovative, research 
oriented methods.

Some of the staff at SFI are excited by DSpace, for example, and we'd 
be interested in helping them explore its use in the lucene/clustering 
context.  That, and their use of Dublin Core for cataloging their 
future work might be of general interest here in the mail list too.

> > We did do a
>> quick LSA SVD on a random set of the papers to see what the 
>> performance (both CPU and good clustering) would be like.  Our 
>> results are encouraging, and I think the frequent phrases approach 
>> would be best for this collection.
> It is always going to be challanging if you attempt to cluster the 
> entire collection, you know. I'm (or rather: I will be) working on 
> algorithm's extensions to deal with full text documents.

We're mainly using Abstracts and other meta data (Title, Authors, Key 
phrases, Abstracts, Dates, and so on).  These are reasonably small: 
Abstracts are 150 words on the average over the current 1122 document 
collection.  If we include the title and key phrases, we get 172 

I suspect we could safely limit the abstracts to the first few 
sentences too, getting us to a much smaller number.  Indeed, if we 
tossed the abstracts altogether, and used just titles and key phrases, 
we're down to less than 20 words/doc!  I bet simply using reasonable 
preprocessing we could get small enough "snippets" as to be workable.

> Dawid

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