<div dir="ltr">Hi Professor Pederson,<div><br></div><div style>Me and Mona Diab created a new sense similarity metric <i>wmfvec</i>, which is a latent vector version of Extended Lesk based on weighted matrix factorization. We evaluated on the all-words WSD tasks.</div>
<div style><br></div><div style>You can find the similarity package at:</div><div style><a href="http://www.cs.columbia.edu/~weiwei/code.html#wmfvec">http://www.cs.columbia.edu/~weiwei/code.html#wmfvec</a><br></div><div style>
<br></div><div style>The paper is:</div><div style>[1] Weiwei Guo and Mona Diab. 2012. Learning the latent semantics of a concept by its definition. In proceedings of ACL 2012.<a href="http://aclweb.org/anthology//P/P12/P12-2028.pdf">http://aclweb.org/anthology//P/P12/P12-2028.pdf</a></div>
<div style><br></div><div style>best</div></div><div class="gmail_extra"><br clear="all"><div><div dir="ltr"><div>Weiwei Guo,<br></div>Columbia University,<div><a href="http://www.cs.columbia.edu/~weiwei" target="_blank">www.cs.columbia.edu/~weiwei</a></div>
<div><br></div></div></div>
<br><br><div class="gmail_quote">On Mon, Oct 7, 2013 at 9:44 AM, Eneko Agirre <span dir="ltr"><<a href="mailto:e.agirre@ehu.es" target="_blank">e.agirre@ehu.es</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
<div><br>
<br>
Hi Ted and all,<br>
<br>
you might want to check
<a href="http://ixa2.si.ehu.es/ukb/" target="_blank">http://ixa2.si.ehu.es/ukb/</a>,
a graph-based algorithm for WSD and similarity,which uses random
walks. It scores very high in RG65 and WordSim353 when run on
WordNet, and can be applied to any KB.<br>
<br>
It's open source and includes all data necessary to replicate the
results reported in the following:<br>
<br style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;font-weight:normal;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">
<span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">[3] Eneko Agirre, Enrique
Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca and Aitor
Soroa. 2009. A Study on Similarity and Relatedness Using
Distributional and WordNet-based Approaches. Proceedings of
NAACL-HLT 09. Boulder, USA. (</span><a href="https://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1239169991/publikoak/2009-naacl-long.pdf" style="font-family:'Times New Roman';font-size:medium;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px" target="_blank">PDF</a><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">)</span><br style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;font-weight:normal;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">
<br style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;font-weight:normal;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">
<span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">[4] Eneko Agirre, Montse
Cuadros, German Rigau and Aitor Soroa. 2010. Exploring
Knowledge Bases for Similarity. Proceedings of LREC 2010.
Valletta, Malta. (</span><a href="http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1274099085/publikoak/main.pdf" style="font-family:'Times New Roman';font-size:medium;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px" target="_blank">PDF</a><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">)</span><br style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:start;font-style:normal;font-weight:normal;line-height:normal;text-transform:none;font-size:medium;white-space:normal;font-family:'Times New Roman';word-spacing:0px">
<br>
best<br>
<br>
eneko<br>
<br>
<br>
<br>
10/06/2013 05:45 PM(e)an, Ted Pedersen(e)k idatzi zuen:<br>
</div><div><div class="h5">
<blockquote type="cite">
<pre>Greetings all,
I'm preparing a tutorial on measuring semantic similarity and
relatedness between concepts, My particular focus is on methods that
do this using ontologies or other (at least somewhat) structured
resources (like Wikipedia, folksonomies, etc.) and that also have
freely available software associated with them (or at least a web
demo).
While it's a very interesting area, this particular tutorial won't
include purely distributional approaches (due to time constraints), so
I'm looking for methods and software that use some sort of resource
like WordNet, Wikipedia, medical ontologies, Freebase, etc. to arrive
at measurements of semantic similarity or relatedness between pairs of
concepts.
What follows is my current list, based not only on projects I have
heard of but have used in the not too distant past - so I guess I'm
particularly interested in projects you have used or created yourself
(and can therefore vouch for to some extent).
Based on WordNet, provide path, depth, info content based measures,
may include relatedness measures like lesk, vector, hso
WordNet::Similarity
<a href="http://wn-similarity.sourcforge.net" target="_blank">http://wn-similarity.sourcforge.net</a>
NLTK
<a href="http://nltk.org" target="_blank">http://nltk.org</a>
ws4j
<a href="https://code.google.com/p/ws4j/" target="_blank">https://code.google.com/p/ws4j/</a>
Based on UMLS (Unified Medical Language System), provide path, depth,
info content measures, includes relatedness measures lesk, vector
UMLS::Similarity
<a href="http://umls-similarity.sourceforge.net" target="_blank">http://umls-similarity.sourceforge.net</a>
Based on (GO), provide path, depth, and info content measures
Proteinon
<a href="http://lasige.di.fc.ul.pt/webtools/proteinon/" target="_blank">http://lasige.di.fc.ul.pt/webtools/proteinon/</a>
I will post a summary of whatever I hear about after some period of
time. Any hints or suggestions will be very gratefully received.
Many thanks,
Ted
</pre>
</blockquote>
<br>
<br>
</div></div><span class="HOEnZb"><font color="#888888"><pre cols="72">--
Eneko Agirre
Euskal Herriko Unibertsitatea
University of the Basque Country
<a href="http://ixa2.si.ehu.es/eneko" target="_blank">http://ixa2.si.ehu.es/eneko</a> </pre>
</font></span></div>
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