<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><br></div><div>Hello,</div><div><br></div><div>I am also interested in the topic, but in a simpler way. I would like to measure word-based (not sense-based) similarity - that is, sentences that share the same words (lemmas), excluding stopwords. As I need to preprocess twitter sentiment corpora, I was wondering if there are tools to detect word similarity, as in spam or repetitive twitter messages. Does anybody know anything for Spanish?</div><div><br></div><div>Thank you very much,</div><div><br></div><div>Juan F.</div><div><br></div><div><br></div><br><div><div>El 07/10/2013, a las 15:44, Eneko Agirre escribió:</div><br class="Apple-interchange-newline"><blockquote type="cite"> <meta content="text/html; charset=ISO-8859-1" http-equiv="Content-Type"> <div bgcolor="#FFFFFF" text="#000000"> <div class="moz-cite-prefix"><br> <br> Hi Ted and all,<br> <br> you might want to check <meta http-equiv="content-type" content="text/html; charset=ISO-8859-1"> <a href="http://ixa2.si.ehu.es/ukb/">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="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;"> <span style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; display: inline !important; float: none;">[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; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;">PDF</a><span style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; display: inline !important; float: none;">)</span><br style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;"> <br style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;"> <span style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; display: inline !important; float: none;">[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; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;">PDF</a><span style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; display: inline !important; float: none;">)</span><br style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 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> <blockquote cite="mid:CAAfu72_ft9fYxUxZbJBud8r5sDtPXDJETMiyfwvnQ8to5v-rOg@mail.gmail.com" type="cite"> <pre wrap="">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 class="moz-txt-link-freetext" href="http://wn-similarity.sourcforge.net">http://wn-similarity.sourcforge.net</a>
NLTK
<a class="moz-txt-link-freetext" href="http://nltk.org">http://nltk.org</a>
ws4j
<a class="moz-txt-link-freetext" href="https://code.google.com/p/ws4j/">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 class="moz-txt-link-freetext" href="http://umls-similarity.sourceforge.net">http://umls-similarity.sourceforge.net</a>
Based on (GO), provide path, depth, and info content measures
Proteinon
<a class="moz-txt-link-freetext" href="http://lasige.di.fc.ul.pt/webtools/proteinon/">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> <pre class="moz-signature" cols="72">--
Eneko Agirre
Euskal Herriko Unibertsitatea
University of the Basque Country
<a class="moz-txt-link-freetext" href="http://ixa2.si.ehu.es/eneko">http://ixa2.si.ehu.es/eneko</a> </pre> </div> _______________________________________________<br>UNSUBSCRIBE from this page: <a href="http://mailman.uib.no/options/corpora">http://mailman.uib.no/options/corpora</a><br>Corpora mailing list<br><a href="mailto:Corpora@uib.no">Corpora@uib.no</a><br>http://mailman.uib.no/listinfo/corpora<br></blockquote></div><br></body></html>