[Corpora-List] software for measuring semantic similarity and relatedness?

Wed Oct 30 02:06:27 UTC 2013

G'day,

I think these summaries are great!  Have you considered adding them to
the aclwiki?  I think it is a good place to make these widely
available (I try to keep the Japanese corpora page up-to-date).

On Wed, Oct 30, 2013 at 2:39 AM, manaal faruqui <manaalfar at gmail.com> wrote:
> I have recently assembled (under  construction) a list of all the available
> lexical semantic evaluation benchmarks that people have been using in their
> research. Hope people will find it useful!
>
> http://www.cs.cmu.edu/~mfaruqui/suite.html
>
> Manaal
>
>
> On Tue, Oct 29, 2013 at 2:54 PM, Ted Pedersen <tpederse at d.umn.edu> wrote:
>>
>> Thanks to all who responded to my request for information about freely
>> available packages to compute semantic similarity and relatedness
>> using some sort of ontology or structured resource.
>>
>> Below is my best attempt at a summary - I have tried to be accurate
>> here, but please if I've errored in how something is described (or
>> have messed up a URL) please do let me know. And of course, if there
>> are additions that should be made to this list, I'd be more than happy
>> to learn of those and include both in this list and in the tutorial
>> that motivated my original request. And my sincere apologies if
>> someone sent me something that isn't included here - as long as there
>> was an implementation that could be downloaded or accessed via the
>> web, I intended to include that here (so please don't hesitate to
>> remind me).
>>
>> I've divided the responses up into three categories.
>>
>> 1) packages that provide a variety of measures (and normally include
>> multiple measures that were developed by someone else, and then
>> implemented by the package authors perhaps along with a few of their
>> own measures)
>>
>> 2) implementations of specific measures
>>
>> 3) gold standard human similarity and relatedness judgements
>>
>> Note that 3) wasn't included in my original request, but came about as
>> a result of asking about the first two, so I thought I would include
>> that information as well.
>>
>> ================================================
>> Systems that provide a variety of measures :
>> ================================================
>>
>> Based on WordNet and include measures based on path length, depth,
>> information content, and may include relatedness measures like lesk,
>> vector, hso
>>
>> 1) WordNet::Similarity http://wn-similarity.sourceforge.net
>>
>> 2) NLTK http://nltk.org
>>
>> 3) ws4j https://code.google.com/p/ws4j/
>>
>> 4) DKPro https://code.google.com/p/dkpro-similarity-asl/ (also
>> includes support for Wikipedia/Wikirelate, Wiktionary, openThesaurus,
>> GermaNet)
>>
>> Based on various medical ontologies
>>
>> 1) UMLS::Similarity http://umls-similarity.sourceforge.net (based on
>> Unified Medical Language System)
>>
>> 2) Proteinon http://lasige.di.fc.ul.pt/webtools/proteinon/ (based on
>> Gene Ontology)
>>
>> Systems where the focus may be on other issues but that still include
>> some support of semantic similarity and relatedness measures between
>> words/concepts
>>
>> 1) Disco http://www.linguatools.de/disco/disco_en.html (co-occurrence
>> / corpus based similarity, but also includes plug-in for ontologies in
>> Protege)
>>
>> 2) Semilar http://semanticsimilarity.org/ (text to text similarity but
>> also includes support for word to word similarity)
>>
>> =================================================
>> Implementations of Specific measures :
>> =================================================
>>
>> 1) UKB http://ixa2.si.ehu.es/ukb/ (graph based similarity and
>> relatedness, using WordNet)
>>
>> 2) http://www.cs.columbia.edu/~weiwei/code.html#wmfvec (high
>> dimensional approach using definitions from WordNet/Wiktionary)
>>
>> 3) http://olesk.com/#SemanticRelatedness (shortest path in weighted
>> semantic network)
>>
>>
>> ==============================================================================
>> Gold Standard data sets with human similarity and relatedness judgements :
>>
>> ==============================================================================
>>
>> 1) Yang and Powers 2006 Verb Similarity Scores (130 pairs)
>>
>> http://david.wardpowers.info/Research/AI/papers/200601-GWC-VerbSimWN.pdf
>>
>> http://david.wardpowers.info/Research/AI/papers/200601-GWC-130verbpairs.txt
>>
>> 2) WordSimilarity 353 Test Collection
>>
>> http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/
>>
>> http://alfonseca.org/eng/research/wordsim353.html (divided into
>> similarity and relatedness pairs)
>>
>> 3) Rubenstein and Goodenough (65 pairs) Miller and Charles (30 pair
>> subset of RG)
>>
>> http://www.d.umn.edu/~tpederse/Data/rubenstein-goodenough-1965.txt
>>
>> http://www.d.umn.edu/~tpederse/Data/miller-charles-1991.txt
>>
>> 4) ConceptSim (sense annotated versions of MC,RG, and WordSim 353)
>>
>> http://www.seas.upenn.edu/~hansens/conceptSim/
>>
>> 5) Medical concepts from UMLS
>>
>> http://rxinformatics.umn.edu/SemanticRelatednessResources.html
>>
>> Four different data sets, one with 101 pairs, another made up of a
>> subset of 30 of those (both rated for relatedness), annother with 566
>> pairs rated for similarity, and another with 587 pairs rated for
>> relatedness.
>>
>> ========================================================================
>>
>> So, that's what I have at this point. Additional contributions,
>> clarifications, etc. are certainly welcomed!
>>
>> Cordially,
>> Ted
>>
>> On Sun, Oct 6, 2013 at 10:50 AM, Ted Pedersen <tpederse at d.umn.edu> wrote:
>> > Well I managed to misspell my own URL :)
>> >
>> > WordNet::Similarity
>> > http://wn-similarity.sourceforge.net
>> >
>> > All the others appear to be correct.
>> >
>> > On Sun, Oct 6, 2013 at 10:45 AM, Ted Pedersen <tpederse at d.umn.edu>
>> > wrote:
>> >> 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
>> >> http://wn-similarity.sourcforge.net
>> >>
>> >> NLTK
>> >> http://nltk.org
>> >>
>> >> ws4j
>> >> https://code.google.com/p/ws4j/
>> >>
>> >> Based on UMLS (Unified Medical Language System), provide path, depth,
>> >> info content measures, includes relatedness measures lesk, vector
>> >>
>> >> UMLS::Similarity
>> >> http://umls-similarity.sourceforge.net
>> >>
>> >> Based on (GO), provide path, depth, and info content measures
>> >>
>> >> Proteinon
>> >> http://lasige.di.fc.ul.pt/webtools/proteinon/
>> >>
>> >> 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
>> >>
>> >> --
>> >> Ted Pedersen
>> >> http://www.d.umn.edu/~tpederse
>> >
>> >
>> >
>> > --
>> > Ted Pedersen
>> > http://www.d.umn.edu/~tpederse
>>
>>
>>
>> --
>> Ted Pedersen
>> http://www.d.umn.edu/~tpederse
>>
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-- 
Francis Bond <http://www3.ntu.edu.sg/home/fcbond/>
Division of Linguistics and Multilingual Studies
Nanyang Technological University

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