This document summarizes a tutorial on measuring the similarity and relatedness of concepts. It discusses the distinction between semantic similarity and relatedness. It describes several common measures of similarity that use information from ontologies, such as path-based measures, measures that incorporate path and depth, and measures that incorporate information content. It also discusses measures of relatedness that can be used for concepts that are not connected by ontological relations, such as definition-based measures and measures based on gloss vectors constructed from corpus data. Experimental results generally show that gloss vector measures perform best, followed by definition-based measures, with path-based measures performing the worst.

1. Measuring the Similarity and
Relatedness of Concepts :
a MICAI 2013 Tutorial
Ted Pedersen, Ph.D.
University of Minnesota
Department of Computer Science, Duluth
http://www.d.umn.edu/~tpederse
tpederse@d.umn.edu

2. What (I hope) you will learn!
●
●
●
●
●
The distinction between semantic similarity
and relatedness (and why both are useful)
How to measure using information from
ontologies, definitions, and corpora
How to use freely available software
How to conduct experiments using freely
available reference standards
Some applications where these measures are
used or could be useful
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3. Orientation
●
●
We focus on methods that measure similarity
and relatedness using information found in an
ontology, which may be possibly augmented
with statistics from corpora or other resources
We will not discuss purely distributional
methods
–
November 25, 2013
Very interesting and useful, and deserve
their own separate tutorial
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4. Just a few distributional methods
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Latent Semantic Analysis
–
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SenseClusters
–
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http://senseclusters.sourceforge.net
Clustering by Committee
–
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http://lsa.colorado.edu
http://demo.patrickpantel.com
Disco
–
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http://www.linguatools.de/disco/disco_en.html
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5. Outline
●
Measures of Similarity and Relatedness
–
●
Using Open Source Software
–
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75 minutes + 10 minutes of questions
45 minutes + 10 minutes of questions
Similarity and Relatedness in the Wild
–
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60 minutes + 10 minutes of questions
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6. Measures of Semantic
Similarity and Relatedness
(without tears)
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7. What are we measuring?
●
Concept pairs (word senses)
–
●
Assign a numeric value that quantifies how
similar or related two concepts are
Not words
–
Cold may be temperature or illness
●
–
This tutorial assumes senses assigned
●
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Word Sense Disambiguation
But, can also use these measures for WSD!
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8. Why?
●
●
Being able to organize concepts by their
similarity or relatedness to each other is a
fundamental operation in the human mind, and
in many problems in Natural Language
Processing and Artificial Intelligence
If we know a lot about X, and if we know Y is
similar to X, then a lot of what we know about
X may apply to Y
–
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Use X to explain or categorize Y
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9. What is lefse?
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10. Well, it's like a tortilla, except made with potatoes.
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11. Lefse is a traditional soft, Norwegian flatbread.
Lefse is made out of flour, and milk or cream (or
sometimes lard), and cooked on a griddle.
Traditional lefse does not include potato, but it is
commonly added to make a thicker dough that is
easier to work with. Special tools are available for
lefse baking, including long wooden turning sticks
and special rolling pins with deep grooves.
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12. Lefse
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13. Similar or Related?
●
Similarity based on is-a relations
–
–
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How much is X like Y?
Share ancestor in is-a hierarchy
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14. Is-a hierarchy
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15. Similar or Related?
●
Similarity based on is-a relations
–
Share ancestor in is-a hierarchy
●
●
–
A miter saw and a sander are similar
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LCS : least common subsumer
Closer / deeper the ancestor the more similar
both are kinds-of power tools (LCS)
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16. Least Common Subsumer (LCS)
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17. Saws!
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18. Sanders!
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19. Similar or Related?
●
Relatedness more general
–
How much is X related to Y?
–
Many ways to be related
●
●
Hammer and nail are related but they really
aren't similar
–
●
is-a, part-of, treats, affects, symptom-of, ...
(use hammer to drive nails)
All similar concepts are related, but not all
related concepts are similar
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20. “Standard” Measures of Similarity
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Path Based
–
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Rada et al., 1989 (path)
Path + Depth
–
–
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Wu & Palmer, 1994 (wup)
Leacock & Chodorow, 1998 (lch)
Path + Information Content
–
Resnik, 1995 (res)
–
Jiang & Conrath, 1997 (jcn)
–
Lin, 1998 (lin)
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21. Path Based Measures
●
●
Distance between concepts (nodes) in tree
intuitively appealing
Spatial orientation, good for networks or maps
but not is-a hierarchies
–
–
Assumes all paths have same “weight”
–
●
Reasonable approximation sometimes
But, more specific (deeper) paths tend to
travel less semantic distance
Shortest path a good start, needs corrections
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22. Shortest is-a Path
1
●
path(a,b) =
-----------------------------shortest is-a path(a,b)
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23. We count nodes...
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Maximum = 1
–
–
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self similarity
path(miter saw, miter saw) = 1
Minimum = 1 / (longest path in isa tree)
–
path(miter saw, claw hammer) = 1/7
–
path(table saw, flat tip screwdriver) = 1/7
–
etc...
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24. path(miter saw, sander) = .25
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25. miter saw & sander
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26. path (hammer, power tool) = .25
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27. hammers and power tools
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28. ?
●
●
Are hammer and power tool similar to the
same degree as are mitre saw and sander?
The path measure reports “yes, they are.”
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29. Path + Depth
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Path only doesn't account for specificity
●
Deeper concepts more specific
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Paths between deeper concepts travel less
semantic distance
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30. Wu and Palmer, 1994
2 * depth (LCS (a,b))
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wup(a,b) = ---------------------------depth (a) + depth (b)
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depth(x) = shortest is-a path(root,x)
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31. wup(miter saw, sander) = (2*2)/(4+3) = .57
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32. wup (hammer, power tool) = (2*1)/(2+3) = .4
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33. ?
●
●
●
Wu and Palmer reports that sander and miter
saw (.57) are more similar than are power tool
and hammer (.4)
Path reports that sander and miter saw (.25)
are equally similar as are power tool and
hammer (.25)
Note that measures are scaled differently and
so should compare relative rankings between
measures (and not exact scores)
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34. Information Content
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ic(concept) = -log p(concept) [Resnik 1995]
–
–
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Term frequency +Inherited frequency
–
●
Need to count concepts
p(concept) = tf + if / N
Depth shows specificity but not frequency
Low frequency concepts often much more
specific than high frequency ones
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35. Information Content
term frequency (tf)
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36. Information Content
inherited frequency (if)
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37. Information Content (IC = -log (f/N)
final count (f = tf + if, N = 365,820)
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38. Information Content (IC = -log (f/N)
final count (f = tf + if, N = 365,820)
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39. Lin, 1998
2 * IC (LCS (a,b))
●
lin(a,b) = -------------------------IC (a) + IC (b)
●
Look familiar?
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40. Wu & Palmer, 1994
●
●
2 * depth (LCS (a,b))
wup(a,b) = -------------------------depth (a) + depth (b)
wup and lin are identical except that lin
uses info content instead of depth
– Info content provides a measure of
depth (based on specificity)
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41. lin (miter saw, sander) =
2 * 2.26 / (3.60 + 3.66) = 0.62
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42. lin (hammer, power tool) =
2 * 0.71 / (2.26+2.81) = 0.28
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43. ?
●
●
●
Lin : miter saw and sander (.62) more similar
than hammer and power tool (.28)
Wu and Palmer : miter saw and sander (.57)
more similar than hammer and power tool (.4)
Path miter saw and sander (.25) equally
similar to hammer and power tool (.25)
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44. What about concepts not connected
via is-a relations?
●
Connected via other relations?
–
●
Part-of, treatment-of, causes, etc.
Not connected at all?
–
–
●
In different sections (axes) of an ontology
In different ontologies entirely
Relatedness!
–
Use definition information
–
No is-a relations so can't be similarity
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45. Measures of relatedness
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Path based
–
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Hirst & St-Onge, 1998 (hso)
Definition based
–
Lesk, 1986
–
Adapted lesk (lesk)
●
●
Banerjee & Pedersen, 2003
Definition + corpus
–
Gloss Vector (vector, vector_pairs)
●
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Patwardhan & Pedersen, 2006
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46. Path based relatedness
●
●
Ontologies include relations other than is-a
These can be used to find shortest paths
between concepts
–
However, a path made up of different kinds
of relations can lead to big semantic jumps
–
A hammer is used to drive nails which are
made of iron which comes from mines in
Minnesota
●
November 25, 2013
…. so hammer and Minnesota are related ??
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47. Measuring relatedness with definitions
●
●
●
Related concepts defined using many of the
same terms
But, definitions are short, inconsistent
Concepts don't need to be connected via
relations or paths to measure them
–
Lesk, 1986
–
Adapted Lesk, Banerjee & Pedersen, 2003
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48. Two separate ontologies...
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49. Could join them together … ?
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50. Each concept has definition
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51. Each concept has definition
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52. Each concept has definition
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53. Overlaps
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Claw hammer and carpenter
–
Related by working with wood
●
●
●
Can't see this in structure of is-a hierarchies
Claw hammer and iron worker just as similar
Ball peen hammer and claw hammer
–
Reflects structure of is-a hierarchies
–
If you start with text like this maybe you can
build is-a hierarchies automatically!
●
November 25, 2013
Another tutorial...
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54. Lesk and Adapted Lesk
●
Lesk, 1986 : measure overlaps in definitions to
assign senses to words
–
●
The more overlaps between two senses
(concepts), the more related
Banerjee & Pedersen, 2003, Adapted Lesk
–
Augment definition of each concept with
definitions of related concepts
●
–
November 25, 2013
Build a super gloss
Increase chance of finding overlaps
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55. The problem with definitions ...
●
Definitions contain variations of terminology that
make it impossible to find exact overlaps
●
spatula : an instrument for spreading material
●
spreader : a hand tool for smoothing compounds
●
No matches??! How can we see that “hand tool”
and “instrument” are similar, as are “spreading
material” and “smoothing compound” ?
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56. Gloss Vector Measure
of Semantic Relatedness
●
Rely on co-occurrences of terms
–
Terms that occur within some given number
of terms of each other other
●
Allows for a fuzzier notion of matching
●
Exploits second order co-occurrences
–
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Friend of a friend relation
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57. Gloss Vector Measure
of Semantic Relatedness
●
Friend of a friend relation
–
Suppose hand tool and instrument don't
occur in text with each other. But, suppose
that “repair” occurs with each.
–
Hand tool and instrument are second order
co-occurrences via “repair”
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58. Gloss Vector Measure
of Semantic Relatedness
●
●
●
●
Replace words or terms in definitions with
vector of co-occurrences observed in corpus
Defined concept now represented by an
averaged vector of co-occurrences
Measure relatedness of concepts via cosine
between their respective vectors
Patwardhan and Pedersen, 2006
–
November 25, 2013
Inspired by Schutze, 1998
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59. Experimental Results
●
Vector > Lesk > Info Content > Depth > Path
–
●
Clear trend across various studies
Big differences in intrinsic evaluations (Vector
> Lesk >> Info Content > Depth > Path)
–
–
●
Banerjee and Pedersen, 2003 (IJCAI)
Pedersen, et al. 2007 (JBI)
Smaller differences in extrinsic evaluations
–
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Human raters mix up similarity &
relatedness?
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60. Questions?
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61. References
●
●
●
S. Banerjee and T. Pedersen. Extended gloss overlaps as a
measure of semantic relatedness. In Proceedings of the Eighteenth
International Joint Conference on Artificial Intelligence, pages 805810, Acapulco, August 2003. (lesk)
J. Jiang and D. Conrath. Semantic similarity based on corpus
statistics and lexical taxonomy. In Proceedings on International
Conference on Research in Computational Linguistics, pages 1933, Taiwan, 1997. (jcn)
C. Leacock and M. Chodorow. Combining local context and
WordNet similarity for word sense identification. In C. Fellbaum,
editor, WordNet: An electronic lexical database, pages 265-283.
MIT Press, 1998. (lch)
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62. References
●
●
●
M.E. Lesk. Automatic sense disambiguation using machine
readable dictionaries: how to tell a pine code from an ice cream
cone. In Proceedings of the 5th annual international conference on
Systems documentation, pages 24-26. ACM Press, 1986.
D. Lin. An information-theoretic definition of similarity. In
Proceedings of the International Conference on Machine Learning,
Madison, August 1998. (lin).
S. Patwardhan and T. Pedersen. Using WordNet-based Context
Vectors to Estimate the Semantic Relatedness of Concepts. In
Proceedings of the EACL 2006 Workshop on Making Sense of
Sense: Bringing Computational Linguistics and Psycholinguistics
Together, pages 1-8, Trento, Italy, April 2006. (vector, vector_pairs)
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63. References
●
●
●
R. Rada, H. Mili, E. Bicknell, and M. Blettner. Development and
application of a metric on semantic nets. IEEE Transactions on
Systems, Man and Cybernetics, 19(1):17-30, 1989. (path)
P. Resnik. Using information content to evaluate semantic similarity
in a taxonomy. In Proceedings of the 14th International Joint
Conference on Artificial Intelligence, pages 448-453, Montreal,
August 1995. (res)
H. Schütze. Automatic word sense discrimination. Computational
Linguistics, 24(1):97-123, 1998.
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64. Using Open Source Software
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65. Using Open Source Software
●
Packages providing the “standard” measures
●
Implementations of specific measures
●
Overview of WordNet::Similarity usage
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66. Software that provides
some of the “standard” measures
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67. WordNet::Similarity
●
Similarity and Relatedness for WordNet
–
http://wordnet.princeton.edu
●
Written in Perl (starting in 2002)
●
Offers command line, web interface, and API
–
●
http://wn-similarity.sourceforge.net
We'll come back to this for some examples
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68. ws4j
●
Java Re-implementation of WordNet::Similarity
–
●
●
https://code.google.com/p/ws4j/
Includes path, depth, info content, hso, and
lesk measures
Online demo
–
November 25, 2013
http://ws4jdemo.appspot.com/
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69. NLTK
●
Natural Language Toolkit
–
●
Includes path, depth, and information
content measures
Written in Python
–
General purpose NLP toolkit
●
–
November 25, 2013
Parsers, part of speech taggers, and more
http://nltk.org/
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70. DKPro Similarity
●
Semantic similarity using vector space models
like LSA and ESA, and also WordNet
–
–
●
●
Implemented using UIMA
https://code.google.com/p/dkpro-similarity-asl/
Part of the much larger DKPro project, which
provides UIMA wrappers for many existing
tools and models
Supports measuring similarity of short texts and
concept pairs
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71. Semilar
●
Semantic similarity using WordNet and LSA
–
●
●
●
http://semanticsimilarity.org
Supports measuring similarity of short texts
and concept pairs
Provides many pre-built models using LSA
Includes a web service and API in addition to
downloadable libraries
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72. UMLS::Similarity
●
Ports WordNet::Similarity to the UMLS
–
Unified Medical Language System from
NLM, a data warehouse of medical sources
●
–
●
Freely available, license required
http://umls-similarity.sourceforge.net
Perl and mySQL
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73. ProteInOn
●
Computes Semantic Similarity for the Gene
Ontology (GO) using path and information
content measures
–
●
http://geneontology.org/
Protein Interactions and Ontology
–
November 25, 2013
http://lasige.di.fc.ul.pt/webtools/proteinon/
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74. Measure Specific Software
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75. UKB
●
Graph based similarity and relatedness
measures, using WordNet
–
●
http://ixa2.si.ehu.es/ukb/
Applies Personalized Page Rank to semantic
similarity and relatedness measures, as well
as to word sense disambiguation
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76. WMFVEC
●
High dimensional vector approach using
definitions from WordNet and Wiktionary
–
●
http://www.cs.columbia.edu/~weiwei/code.h
tml#wmfvec
Supports similarity measurements of short
texts and concept pairs
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77. olesk
●
Shortest path in weighted semantic network
–
●
http://olesk.com/#SemanticRelatedness
Supports measuring similarity of short texts
and concept pairs
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78. Illinois WNSim
●
WordNet-based Similarity Metric
–
–
–
●
https://cogcomp.cs.illinois.edu/page/softwa
re_view/Illinois%20WNSim
Also provides Java version
https://cogcomp.cs.illinois.edu/page/softw
are_view/Illinois%20WNSim%20(Java
)
Measures similarity of short texts, provides
support for similarity of named entities
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79. WordNet::Similarity Usage
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80. WordNet::Similarity
●
Install WordNet (http://princeton.wordnet.edu)
–
●
Make sure to set $WNHOME
Install WordNet::QueryData
–
–
●
cpan
> install WordNet::QueryData
Install WordNet::Similarity
–
cpan
–
> install WordNet::Similarity
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81. WordNet::Similarity
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82. Command line
●
similarity.pl --type WordNet::Similarity::path dog cat
–
●
similarity.pl --type WordNet::Similarity::path dog#n cat#n
–
●
dog#n#1 cat#n#1 0.2
similarity.pl --type WordNet::Similarity::path dog#n#2 cat#n#3
–
●
dog#n#1 cat#n#1 0.2
dog#n#2 cat#n#3 0.125
Similarity.pl –type WordNet::Similarity::path dog cat#n#3
–
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dog#n#3 cat#n#3 0.142857142857143
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83. Command line
●
similarity.pl --type WordNet::Similarity::path dog cat --allsenses
–
–
dog#n#3 cat#n#2 0.2
–
dog#n#1 cat#n#7 0.2
–
dog#n#7 cat#n#5 0.166666666666667
–
dog#n#4 cat#n#2 0.142857142857143
–
dog#n#3 cat#n#3 0.142857142857143
–
dog#v#1 cat#v#2 0.142857142857143
–
dog#n#4 cat#n#3 0.142857142857143
–
dog#n#6 cat#n#5 0.142857142857143
–
dog#v#1 cat#v#1 0.142857142857143
–
dog#n#2 cat#n#2 0.125
–
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dog#n#1 cat#n#1 0.2
Etc...
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84. WordNet senses
●
wn cat -over
●
Overview of noun cat
●
The noun cat has 8 senses (first 1 from tagged texts)
●
●
●
●
1. (18) cat, true cat -- (feline mammal usually having thick soft fur
and no ability to roar: domestic cats; wildcats)
2. guy, cat, hombre, bozo -- (an informal term for a youth or man; "a
nice guy"; "the guy's only doing it for some doll")
3. cat -- (a spiteful woman gossip; "what a cat she is!")
4. kat, khat, qat, quat, cat, Arabian tea, African tea -- (the leaves of
the shrub Catha edulis which are chewed like tobacco or used to
make tea; has the effect of a euphoric stimulant; "in Yemen kat is
used daily by 85% of adults")
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85. wn cat -over
●
●
●
●
5. cat-o'-nine-tails, cat -- (a whip with nine knotted cords;
"British sailors feared the cat")
6. Caterpillar, cat -- (a large tracked vehicle that is propelled
by two endless metal belts; frequently used for moving earth
in construction and farm work)
7. big cat, cat -- (any of several large cats typically able to roar
and living in the wild)
8. computerized tomography, computed tomography, CT,
computerized axial tomography, computed axial tomography,
CAT -- (a method of examining body organs by scanning them
with X rays and using a computer to construct a series of
cross-sectional scans along a single axis
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86. wn cat -over
●
Overview of verb cat
●
The verb cat has 2 senses (no senses from tagged texts)
●
1. cat -- (beat with a cat-o'-nine-tails)
●
2. vomit, vomit up, purge, cast, sick, cat, be sick, disgorge, regorge,
retch, puke, barf, spew, spue, chuck, upchuck, honk, regurgitate,
throw up -- (eject the contents of the stomach through the mouth;
"After drinking too much, the students vomited"; "He purged
continuously"; "The patient regurgitated the food we gave him last
night")
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87. Measure type
(-type WordNet::Similarity::X)
●
Similarity
–
Shortest Path
●
–
Depth
●
–
wup, lch
Information Content
●
November 25, 2013
path
res, lin, jcn
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88. Measure type
(-type WordNet::Similarity::X)
●
Relatedness
–
Definition
●
–
Definition + Corpus
●
●
–
vector
vector_pairs
Path finding
●
November 25, 2013
lesk
hso
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89. Measure type
(-type WordNet::Similarity::X)
●
Random
–
rand
–
Very useful for experimental comparisons
●
November 25, 2013
How do your results compare with a random
measure of similarity or relatedness??
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90. Similarity measures don't
cross part of speech tags
●
similarity.pl --type WordNet::Similarity::path dog#n cat#v
–
Warning (WordNet::Similarity::path::parseWps()) - dog#n
and cat#v belong to different parts of speech.
–
dog#n#2 cat#v#1 -1000000
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91. Relatedness measures
cross POS boundaries
●
similarity.pl --type WordNet::Similarity::lesk dog#n#1 cat#v#1
–
●
dog#n#1 cat#v#1 20
similarity.pl --type WordNet::Similarity::vector dog#n#1 cat#v#1
–
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dog#n#1 cat#v#1 0.146581307649965
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92. API
●
use WordNet::Similarity::wup;
●
use WordNet::QueryData;
●
my $wn = WordNet::QueryData->new();
●
my $wup = WordNet::Similarity::wup->new($wn);
●
●
my $value = $wup->getRelatedness('dog#n#1', 'cat#n#1');
●
my ($error, $errorString) = $wup->getError();
●
die $errorString if $error;
●
print "dog (sense 1) <-> cat (sense 1) = $valuen";
●
dog (sense 1) <-> cat (sense 1) = 0.866666666666667
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93. API
●
my $wn = WordNet::QueryData->new;
●
use WordNet::Similarity::PathFinder;
●
my $obj = WordNet::Similarity::PathFinder->new ($wn);
●
my $wps1 = 'winston_churchill#n#1';
●
my $wps2 = 'england#n#1';
●
my @paths = $obj->getShortestPath($wps1, $wps2, 'n', 'wps');
●
my ($length, $path) = @{shift @paths};
●
defined $path or die "No path between synsets";
●
print "shortest path between $wps1 and $wps2 is $length edges longn";
●
print "@$pathn";
●
shortest path between winston_churchill#n#1 and england#n#1 is 14 edges long
winston_churchill#n#1 writer#n#1 communicator#n#1 person#n#1 causal_agent#n#1
physical_entity#n#1 object#n#1 location#n#1 region#n#3 district#n#1
administrative_district#n#1 country#n#2 European_country#n#1 england#n#1
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94. Web Interface
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95. Web Interface
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96. Web Interface
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97. Web Interface
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98. Web Interface
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99. Web Interface
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100. Web Interface
●
If you like the web interface, you can run your
own version!
–
similarity_server.pl
–
All necessary html and cgi files included
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101. Other Utilities
●
Build new information content files – by default
counts come from SemCor
–
BNCFreq.pl
–
brownFreq.pl
–
treebankFreq.pl
–
rawtextFreq.pl
●
compounds.pl – list all WordNet compounds
●
wnDepths.pl – list all WordNet depths
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102. Questions?
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102

103. References
●
●
●
●
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. (ukb)
Daniel Bär, Torsten Zesch, and Iryna Gurevych. DKPro Similarity: An
Open Source Framework for Text Similarity, in Proceedings of the 51st
Annual Meeting of the Association for Computational Linguistics: System
Demonstrations, pages 121-126, August 2013, Sofia, Bulgaria. (pdf) (bib)
(dkpro-similarity)
Steven Bird, Ewan Klein, and Edward Loper (2009). Natural Language
Processing with Python. O’Reilly Media Inc. (nltk)
Q. Do and D. Roth and M. Sammons and Y. Tu and V. Vydiswaran,
Robust, Light-weight Approaches to compute Lexical Similarity. Computer
Science Research and Technical Reports, University of Illinois (2009)
(Illionois WNSim)
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104. References
●
●
●
Weiwei Guo and Mona Diab. "Improving Lexical Semantics for Sentential
Semantics: Modeling Selectional Preference and Similar Words in a Latent
Variable Model". In Proceedings of NAACL, 2013, Atlanta, Georgia, USA.
(wmfvec)
Bridget McInnes, Ted Pedersen, and Serguei Pakhomov, UMLS-Interface
and UMLS-Similarity : Open Source Software for Measuring Paths and
Semantic Similarity - Appears in the Proceedings of the Annual
Symposium of the American Medical Informatics Association, Nov 14-18,
2009, pp. 431-435, San Francisco, CA (umls-similarity)
Ted Pedersen, Siddharth Patwardhan and Jason Michelizzi,
WordNet::Similarity - Measuring the Relatedness of Concepts - Appears in
the Proceedings of Fifth Annual Meeting of the North American Chapter of
the Association for Computational Linguistics (NAACL-04), pp. 38-41, May
3-5, 2004, Boston, MA. (wordnet-similarity)
November 25, 2013
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105. References
●
●
Rus, V., Lintean, M., Banjade, R., Niraula, N., and Stefanescu, D.
(2013). SEMILAR: The Semantic Similarity Toolkit. Proceedings of
the 51st Annual Meeting of the Association for Computational
Linguistics, August 4-9, 2013, Sofia, Bulgaria. (semilar)
Reda Siblini and Leila Kosseim (2013). Using a Weighted Semantic
Network for Lexical Semantic Relatedness. In Proceedings of
Recent Advances in Natural Language Processing (RANLP 2013),
September, Hissar, Bulgaria. (olesk)
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106. Similarity and Relatedness in the Wild :
How do we know it's working?
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107. Intrinsic Evaluation
●
●
●
Develop your own measure
Score it using pairs for which human reference
standard is available
Compare correlation between your measure
and established measures
–
Spearman's rank correlation often used
–
rank.pl in Ngram Statistics Package
●
November 25, 2013
http://ngram.sourceforge.net
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108. Intrinsic Evaluation
●
Replication proves to be very difficult!
●
Many factors, see ACL 2013 paper
–
Offspring from Reproduction Problems: What Replication
Failure Teaches Us (Fokkens, van Erp, Postma,
Pedersen, Vossen, and Freire) - Appears in the
Proceedings of the 51st Annual Meeting of the
Association for Computational Linguistics, August 4-9,
2013, pp. 1691-1701, Sofia, Bulgaria.
–
http://aclweb.org/anthology//P/P13/P13-1166.pdf
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109. Reference Standards
●
Rubenstein and Goodenough, 1965
–
–
Assessed by 50 undergraduate students
–
●
65 pairs
http://www.d.umn.edu/~tpederse/Data/ruben
stein-goodenough-1965.txt
Miller and Charles, 1991
–
30 pair subset of R&G
–
Re-assessed by 38 undergraduate students
–
http://www.d.umn.edu/~tpederse/Data/millercharles-1991.txt
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109

110. Rubenstein and Goodenough pairs
no similarity (0.0) – synonyms (4.0)
●
3.94 gem jewel
●
3.92 car automobile
●
3.92 automobile car
●
3.84 gem jewel
●
2.41 brother lad
●
1.66 lad brother
●
2.37 crane implement
●
1.68 crane implement
●
0.04 rooster voyage
●
0.08 rooster voyage
●
0.04 noon string
●
0.08 noon string
–
November 25, 2013
Rubenstein &
Goodenough
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–
Miller & Charles
110

111. Reference Standards
•
WordSim-353, 2002
–
–
200 pairs assessed by 16 subjects
–
●
153 pairs assessed by 13 subjects
Includes the Miller and Charles pairs (reassessed)
http://www.cs.technion.ac.il/~gabr/resources/
data/wordsim353/
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112. Reference Standards
●
Yang and Powers, 2006
●
130 verb pairs
–
Assessed by 2 academic staff and 4
graduate students
–
How related in meaning is the pair?
●
●
–
November 25, 2013
0 for not at all
4 for inseperably related
http://david.wardpowers.info/Research/AI/p
apers/200601-GWC-130verbpairs.txt
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113. Reference standards
●
●
●
Mturk 771, 2012
771 word pairs scored for relatedness by
Mechanical Turkers
At least 20 judgements per pair
–
1 for not related, 5 for highly related
–
50 ratings per Turker
–
http://www2.mta.ac.il/~gideon/mturk771.html
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114. Reference Standards
●
MWE-300, 2012
–
–
Assessed by 5 native speakers on scale of 0
to 1
–
November 25, 2013
300 pairs where 216 are multi-word
expressions and 84 are word pairs
http://adapt.seiee.sjtu.edu.cn/similarity/
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115. Reference Standards
●
●
Rel-122, 2013
Relatedness scores for 122 noun pairs,
created at University of Central Florida
–
Each pair assessed by at least 20
undergraduate students
–
0 for completely unrelated, 4 for strongly
related
–
http://www.cs.ucf.edu/~seansz/rel-122/
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116. Reference standards
●
MayoSRS, 2007
–
101 pairs of medical concepts
–
Assessed by 13 medical coders and 3
physicians, all from Mayo Clinic
●
–
●
1 for not at all related, 4 for nearly
synonymous
MiniMayoSRS – a highly reliable subset of
29 pairs
http://rxinformatics.umn.edu/SemanticRelate
dnessResources.html
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117. Reference standards
UMNSRS, 2010
–
–
●
●
566 pairs of medical concepts assessed for
similarity by 8 medical students / residents
587 pairs of medical concepts assessed for
relatedness by 8 medical students / residents
Assessed on a continuous scale (0 – 1500)
http://rxinformatics.umn.edu/SemanticRelated
nessResources.html
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118. Reference Standards
●
Lexical & Distributional Semantics Evaluation
Benchmarks, maintained by Manaal Faruqui
–
●
http://www.cs.cmu.edu/~mfaruqui/suite.html
ACL Wiki (various datasets for related tasks)
http://aclweb.org/aclwiki/index.php?title=Simi
larity_(State_of_the_art)
http://aclweb.org/aclwiki/index.php?title=Kn
owledge_collections_and_datasets_(English)
SemEval (many related tasks with data)
–
●
–
November 25, 2013
http://aclweb.org/aclwiki/index.php?title=Se
mEval_Portal
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119. Extrinsic Evaluation
●
Synonym Tests
●
Word Sense Disambiguation
●
Semantic Textual Similarity
●
Recognizing Textual Entailment
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120. ESL Synonym Tests
●
Provide one target word in context
●
Select “closest” synonym from a list of 4
●
●
●
Used in previous versions of TOEFL and other
standardized tests
http://aclweb.org/aclwiki/index.php?title=ESL_Synonym_Questions_(State_
of_the_art)
50 question data set available from Peter Turney
–
November 25, 2013
http://www.apperceptual.com/
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121. ESL Synonym Tests
●
●
Stem: "A rusty nail is not as strong as a clean,
new one."
Choices:
–
(a) corroded
–
(b) black
–
(c) dirty
–
(d) painted
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122. ESL Synonym Tests
●
similarity.pl --type WordNet::Similarity::vector --file rusty.txt
●
rusty#a#1 dirty#a#1 0.175879883782967
●
rusty#a#1 painted#a#1 0.0844532311114079
●
rusty#a#1 black#a#2 0.0656019491836669
●
rusty#a#1 corroded#a#1 0.0357324093083641
–
November 25, 2013
:(
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123. TOEFL Synonym Tests
●
Rusty and other words are adjectives
●
Must used relatedness measure
lesk
– vector
– vector_pairs
– hso
Should do word sense disambiguation first
–
●
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124. Word Sense Disambiguation
●
The meanings of words that occur together in
a context will likely be related
–
If a word has multiple senses, it will most
likely be used in the sense that is most
related to the senses of it's neighbors
–
Relatedness seems to matter more than
similarity, unless you have a list
●
November 25, 2013
I have a horse, a cat and a cow at my farm.
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125. Word Sense Disambiguation
●
SenseRelate Hypothesis : Most words in text
will have multiple possible senses and will
often be used with the sense most related to
those of surrounding words
–
He either has a cold or the flu
●
November 25, 2013
Cold not likely to mean air temperature
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126. SenseRelate
●
●
In coherent text words will be used in similar or
related senses, and these will also be related
to the overall topic or mood of a text
First applied to WSD in 2002
–
Banerjee and Pedersen, 2002 (WordNet)
–
Patwardhan et al., 2003 (WordNet)
–
Pedersen and Kolhatkar 2009 (WordNet)
–
McInnes et al., 2011 (UMLS)
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127. Implementations
●
WordNet::SenseRelate
–
AllWords, TargetWord, WordToSet
–
http://senserelate.sourceforge.net
●
●
Includes command line, API, and web
interface
UMLS::SenseRelate
–
AllWords
–
http://search.cpan.org/dist/UMLS-SenseRelat
e/
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128. Web Interface
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129. Web Interface
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129

130. SenseRelate for WSD
●
Assign each word the sense which is most
similar or related to one or more of its
neighbors
–
–
●
Pairwise
2 or more neighbors
Pairwise algorithm results in a trellis much like
in HMMs
–
November 25, 2013
More neighbors adds lots of information and
a lot of computational complexity
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131. SenseRelate - pairwise
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132. SenseRelate – 2 neighbors
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133. General Observations on WSD Results
●
●
●
●
Nouns more accurate; verbs, adjectives, and
adverbs less so
Increasing the window size nearly always
improves performance
Jiang-Conrath measure often a high performer
for nouns (e.g., Patwardhan et al. 2003)
Vector and lesk have coverage advantage
–
November 25, 2013
handle mixed pairs while others don't
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134. SenseRelate Sentiment Classification
●
The underlying sentiment of a text can be
discovered by determining which emotion is
most related to the words in that text.
–
–
Similar to happy? : joyful, ecstatic, ...
–
●
Related to happy? : love, food, success, ...
Pairwise comparisons between emotion and
senses of words in context
Same form as Naive Bayesian model
–
November 25, 2013
WordNet::SenseRelate::WordToSet
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135. SenseRelate - WordToSet
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136. Experimental Results
●
Sentiment classification results in 2011 i2b2
suicide notes challenge were disappointing
(Pedersen, 2012)
–
Suicide notes not very emotional!
–
In many cases reflect a decision made and
focus on settling affairs
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137. Semantic Textual Similarity (STS)
●
How similar (semantically) are 2 texts?
–
–
●
The Senate Select Committee on
Intelligence is preparing a blistering report on
prewar intelligence on Iraq.
American intelligence leading up to the war
on Iraq will be criticized by a powerful US
Congressional committee due to report soon,
officials said today
http://www-nlp.stanford.edu/wiki/STS
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138. Semantic Textual Similarity (STS)
●
Combined distributional and WordNet
information to learn a model from training data
–
●
UKP: Computing Semantic Textual Similarity by Combining
Multiple Content Similarity Measures,
Daniel Bär, Chris Biemann, Iryna Gurevych, and Torsten
Zesch, Semeval 2012
LSA Boosted with WordNet
–
November 25, 2013
UMBC EBIQUITY-CORE: Semantic Textual Similarity Sy
stems
Lushan Han, Abhay L. Kashyap, Tim Finin, James
Mayfield, and Johnathan Weese, *Sem 2013
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139. Recognizing Textual Entailment (RTE)
●
A text entails a hypothesis if a human reading
the text would infer that the hypothesis is true
–
Text : The Christian Science Monitor named
a US journalist kidnapped in Iraq as
freelancer Jill Carroll.
–
Hypothesis: Jill Carroll was abducted in Iraq.
–
Hypothesis: The Christian Science Monitor
kidnapped a freelancer.
November 25, 2013
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140. RTE methods and data
●
Long series of shared tasks
–
–
●
2004 to present
http://aclweb.org/aclwiki/index.php?title=T
extual_Entailment_Resource_Pool
Recognizing that T and H are similar is helpful,
although does not really solve the problem
–
November 25, 2013
Hybrid approaches (like with STS)
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141. Applications
●
Semantic similarity and relatedness are
important components of many NLP
applications
–
Crucial building blocks
–
Interesting to study in their own right
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142. Thank you!
If you have any suggestions for content that
should be added to or changed in this tutorial,
please let me know! Any other comments are
welcome too.
tpederse@d.umn.edu
Questions?
November 25, 2013
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142

143. References
●
●
●
●
S. Banerjee and T. Pedersen. An adapted Lesk algorithm for word sense
disambiguation using WordNet. In Proceedings of the Third International
Conference on Intelligent Text Processing and Computational Linguistics,
pages 136—145, Mexico City, February 2002. (wsd result)
D. Faria, C. Pesquita, F. M. Couto, and A. Falcão, ProteInOn: A Web Tool
for Protein Semantic Similarity, Technical Report, Department of
Informatics, University of Lisbon, 2007 (proteinon)
L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan and G.
Wolfman (2002). Placing Search in Context: The Concept Revisited. ACM
Transactions on Information Systems, 20(1), 116-131. (wordsim-353)
B. McInnes, T. Pedersen, Y. Liu, G. Melton and S. Pakhomov. Knowledgebased Method for Determining the Meaning of Ambiguous Biomedical
Terms Using Information Content Measures of Similarity. Appears in the
Proceedings of the Annual Symposium of the American Medical
Informatics Association, pages 895-904, Washington, DC, October 2011.
(wsd result)
November 25, 2013
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143

144. References
●
●
●
●
G. A. Miller and W. G. Charles (1991). Contextual Correlates of Semantic
Similarity. Language and Cognitive Processes, 6(1), 1-28.
S. Pakhomov, B. McInnes, T. Adam, Y. Liu, T. Pedersen, and G Melton,
Semantic Similarity and Relatedness between Clinical Terms : An
Experimental Study - Appears in the Proceedings of the Annual
Symposium of the American Medical Informatics Association, November
13-17, 2010, pp. 572 - 576, Washington, DC. (umnsrs)
S. Patwardhan, S. Banerjee, and T. Pedersen. Using measures of
semantic relatedness for word sense disambiguation. In Proceedings of
the Fourth International Conference on Intelligent Text Processing and
Computational Linguistics, pages 241—257, Mexico City, February 2003.
(wsd result)
S. Patwardhan and T. Pedersen. Using WordNet-based Context Vectors
to Estimate the Semantic Relatedness of Concepts. In Proceedings of the
EACL 2006 Workshop on Making Sense of Sense: Bringing Computational
Linguistics and Psycholinguistics Together, pages 1-8, Trento, Italy, April
2006. (wsd result)
November 25, 2013
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144

145. References
●
●
●
●
T. Pedersen and V. Kolhatkar. WordNet :: SenseRelate :: AllWords - a
broad coverage word sense tagger that maximizes semantic relatedness.
In Proceedings of the North American Chapter of the Association for
Computational Linguistics - Human Language Technologies 2009
Conference, pages 17-20, Boulder, CO, June 2009. (wsd result)
T. Pedersen, S. Pakhomov, S. Patwardhan, and C. Chute. Measures of
semantic similarity and relatedness in the biomedical domain. Journal of
Biomedical Informatics, 40(3) : 288-299, June 2007. (mayosrs)
T. Pedersen. Rule-based and lightly supervised methods to predict
emotions in suicide notes. Biomedical Informatics Insights, 2012:5 (Suppl.
1):185-193, January 2012. (sentiment result)
H. Rubenstein and J. B. Goodenough (1965). Contextual Correlates of
Synonymy. Communications of the ACM, 8(10), 627-633.
November 25, 2013
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146. References
●
●
●
●
●
SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity, Eneko
Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Semeval 2012 (sts
shared task)
S. Szumlanski, F. Gomez and V.K. Sims (2013). A New Set of Norms for
Semantic Relatedness Measures. Proceedings of the 51st Annual Meeting
of the Association for Computational Linguistics (Volume 2: Short Papers)
(pp. 890-895). Sofia, Bulgaria. (rel-122)
P. D. Turney (2001). Mining the Web for synonyms: PMI-IR versus LSA on
TOEFL. Proceedings of the Twelfth European Conference on Machine
Learning (ECML-2001), Freiburg, Germany, pp. 491-502. (toefl synonyms)
W. Wu, H. Li, H. Wang, and K. Q. Zhu. Probase: a probabilistic taxonomy
for text understanding. In Proceedings of SIGMOD'12, pages 481-492,
2012. (mwe-300)
D. Yang and D.M. W. Powers (2006). Verb Similarity on the Taxonomy of
WordNet. Proceedings of the Third International WordNet Conference
(GWC-06) (pp. 121-128). Jeju Island, Korea.
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147. The End!
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