Difference between revisions of "TOEFL Synonym Questions (State of the art)"

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* '''the TOEFL questions are available on request by contacting [http://lsa.colorado.edu/mail_sub.html LSA Support at CU Boulder]''', the people who manage the [http://lsa.colorado.edu/ LSA web site at Colorado]
 
* TOEFL = Test of English as a Foreign Language
 
* TOEFL = Test of English as a Foreign Language
 
* 80 multiple-choice synonym questions; 4 choices per question
 
* 80 multiple-choice synonym questions; 4 choices per question
−
* TOEFL questions available from [http://www.pearsonkt.com/bioLandauer.shtml Thomas Landauer]
+
* introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring degree of similarity between words
−
* introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring degree of similarity between two words
 
 
* subsequently used by many other researchers
 
* subsequently used by many other researchers
 +
* see also: [[Similarity (State of the art)]]
  
  
Line 96: Line 97:
 
| 64.50%
 
| 64.50%
 
| 53.01–74.88%
 
| 53.01–74.88%
 +
|-
 +
| RI
 +
| Karlgren and Sahlgren (2001)
 +
| Karlgren and Sahlgren (2001)
 +
| Corpus-based
 +
| 72.50%
 +
| 61.38-81.90%
 
|-
 
|-
 
| DS
 
| DS
Line 110: Line 118:
 
| 73.75%
 
| 73.75%
 
| 62.72–82.96%
 
| 62.72–82.96%
 +
|-
 +
| PairClass
 +
| Turney (2008)
 +
| Turney (2008)
 +
| Corpus-based
 +
| 76.25%
 +
| 65.42-85.06%
 
|-
 
|-
 
| HSO
 
| HSO
Line 124: Line 139:
 
| 78.75%
 
| 78.75%
 
| 68.17–87.11%
 
| 68.17–87.11%
 +
|-
 +
| Sa18
 +
| Salle et al. (2018)
 +
| Dobó (2019)
 +
| Corpus-based
 +
| 80.00%
 +
| 69.56–88.11%
 
|-
 
|-
 
| PMI-IR
 
| PMI-IR
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
 +
| Corpus-based
 +
| 81.25%
 +
| 70.97–89.11%
 +
|-
 +
| LC-IR
 +
| Higgins (2005)
 +
| Higgins (2005)
 +
| Web-based
 +
| 81.25%
 +
| 70.97–89.11%
 +
|-
 +
| Do19-corpus
 +
| Dobó (2019)
 +
| Dobó (2019)
 
| Corpus-based
 
| Corpus-based
 
| 81.25%
 
| 81.25%
Line 140: Line 176:
 
|-
 
|-
 
| PPMIC
 
| PPMIC
−
| Bullinaria and Levy (2006)
+
| Bullinaria and Levy (2007)
−
| Bullinaria and Levy (2006)
+
| Bullinaria and Levy (2007)
 
| Corpus-based
 
| Corpus-based
 
| 85.00%
 
| 85.00%
Line 152: Line 188:
 
| 86.25%
 
| 86.25%
 
| 76.73-92.93%
 
| 76.73-92.93%
 +
|-
 +
| SR
 +
| Tsatsaronis et al. (2010)
 +
| Tsatsaronis et al. (2010)
 +
| Lexicon-based
 +
| 87.50%
 +
| 78.21-93.84%
 +
|-
 +
| DC13
 +
| Dobó and Csirik (2013)
 +
| Dobó and Csirik (2013)
 +
| Corpus-based
 +
| 88.75%
 +
| 79.72-94.72%
 +
|-
 +
| Pe14
 +
| Pennington et al. (2014)
 +
| Dobó (2019)
 +
| Corpus-based
 +
| 90.00%
 +
| 81.24-95.58%
 
|-
 
|-
 
| LSA
 
| LSA
Line 159: Line 216:
 
| 92.50%
 
| 92.50%
 
| 84.39-97.20%
 
| 84.39-97.20%
 +
|-
 +
| LSA
 +
| Han (2014)
 +
| Han (2014)
 +
| Hybrid
 +
| 95.0%
 +
| 87.69-98.62%
 +
|-
 +
| ADW
 +
| Pilehvar et al. (2013)
 +
| Pilehvar et al. (2013)
 +
| WordNet graph-based (unsupervised)
 +
| 96.25%
 +
| 89.43-99.22%
 
|-
 
|-
 
| PR
 
| PR
Line 167: Line 238:
 
| 91.26–99.70%
 
| 91.26–99.70%
 
|-
 
|-
 +
| Sp19
 +
| Speer et al. (2017)
 +
| Dobó (2019)
 +
| Hybrid
 +
| 98.75%
 +
| 93.23–99.97%
 +
|-
 +
| Do19-hybrid
 +
| Dobó (2019)
 +
| Dobó (2019)
 +
| Hybrid
 +
| 98.75%
 +
| 93.23–99.97%
 +
|-
 +
| PCCP
 +
| Bullinaria and Levy (2012)
 +
| Bullinaria and Levy (2012)
 +
| Corpus-based
 +
| 100.00%
 +
| 96.32-100.00%
 
|}
 
|}
−
 
  
 
== Explanation of table ==
 
== Explanation of table ==
Line 177: Line 267:
 
* '''Type''' = general type of algorithm: corpus-based, lexicon-based, hybrid
 
* '''Type''' = general type of algorithm: corpus-based, lexicon-based, hybrid
 
* '''Correct''' = percent of 80 questions that given algorithm answered correctly
 
* '''Correct''' = percent of 80 questions that given algorithm answered correctly
−
* '''95% confidence''' = confidence interval calculated using [http://home.clara.net/sisa/onemean.htm Binomial Exact Test]
+
* '''95% confidence''' = confidence interval calculated using the [[Statistical calculators|Binomial Exact Test]]
 
* table rows sorted in order of increasing percent correct
 
* table rows sorted in order of increasing percent correct
 
* several WordNet-based similarity measures are implemented in [http://www.d.umn.edu/~tpederse/ Ted Pedersen]'s [http://www.d.umn.edu/~tpederse/similarity.html WordNet::Similarity] package
 
* several WordNet-based similarity measures are implemented in [http://www.d.umn.edu/~tpederse/ Ted Pedersen]'s [http://www.d.umn.edu/~tpederse/similarity.html WordNet::Similarity] package
 
* LSA = Latent Semantic Analysis
 
* LSA = Latent Semantic Analysis
 +
* PCCP = Principal Component vectors with Caron P
 
* PMI-IR = Pointwise Mutual Information - Information Retrieval
 
* PMI-IR = Pointwise Mutual Information - Information Retrieval
 
* PR = Product Rule
 
* PR = Product Rule
Line 187: Line 278:
 
* CWO = Context Window Overlapping
 
* CWO = Context Window Overlapping
 
* DS = Dependency Space
 
* DS = Dependency Space
 +
* RI = Random Indexing
 +
 +
== Notes ==
 +
 +
* the performance of a corpus-based algorithm depends on the corpus, so the difference in performance between two corpus-based systems may be due to the different corpora, rather than the different algorithms
 +
* the TOEFL questions include nouns, verbs, and adjectives, but some of the WordNet-based algorithms were only designed to work with nouns; this explains some of the lower scores
 +
* some of the algorithms may have been tuned on the TOEFL questions; read the references for details
 +
* Landauer and Dumais (1997) report scores that were corrected for guessing by subtracting a penalty of 1/3 for each incorrect answer; they report a score of 52.5% when this penalty is applied; when the penalty is removed, their performance is 64.4% correct
 +
 +
== References ==
  
 +
Bullinaria, J.A., and Levy, J.P. (2007). [http://www.cs.bham.ac.uk/~jxb/PUBS/BRM.pdf Extracting semantic representations from word co-occurrence statistics: A computational study]. ''Behavior Research Methods'', 39(3), 510-526.
  
−
== Caveats ==
+
Bullinaria, J.A., and Levy, J.P. (2012). [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.228.9582&rep=rep1&type=pdf Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD]. ''Behavior Research Methods'',  44(3):890-907.
  
−
* the performance of a corpus-based algorithm depends on the corpus, so the difference in performance between two corpus-based systems may be due to the different corpora, rather than the different algorithms
+
Dobó, A. (2019). [http://doktori.bibl.u-szeged.hu/10120/1/AndrasDoboThesis2019.pdf A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages]. University of Szeged. [https://github.com/doboandras/dsm-parameter-analysis GitHub repository]
−
* the TOEFL questions include nouns, verbs, and adjectives, but some of the WordNet-based algorithms were only designed to work with nouns
 
  
 +
Dobó, A., and Csirik, J. (2013). [http://link.springer.com/chapter/10.1007/978-3-642-35843-2_42 Computing semantic similarity using large static corpora]. In: van Emde Boas, P. et al. (eds.) ''SOFSEM 2013: Theory and Practice of Computer Science. LNCS, Vol. 7741''. Springer-Verlag, Berlin Heidelberg, pp. 491-502
  
−
== References ==
+
Lushan Han. (2014). [http://ebiquity.umbc.edu/paper/html/id/658/Schema-Free-Querying-of-Semantic-Data Schema Free Querying of Semantic Data], Ph.D. dissertation, University of Maryland, Baltimore County, Baltimore, MD USA.
  
−
Bullinaria, J.A., and Levy, J.P. (2006). [http://www.cs.bham.ac.uk/~jxb/PUBS/BRM.pdf Extracting semantic representations from word co-occurrence statistics: A computational study]. To appear in ''Behavior Research Methods'', 38.
+
Higgins, D. (2005). [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.1517 Which Statistics Reflect Semantics? Rethinking Synonymy and Word Similarity.] In: Kepser, S., Reis, M. (eds.) ''Linguistic Evidence: Empirical, Theoretical and Computational Perspectives''. Mouton de Gruyter, Berlin, pp. 265–284.
  
 
Hirst, G., and St-Onge, D. (1998). [http://mirror.eacoss.org/documentation/ITLibrary/IRIS/Data/1997/Hirst/Lexical/1997-Hirst-Lexical.pdf Lexical chains as representation of context for the detection and correction of malapropisms]. In C. Fellbaum (ed.), ''WordNet: An Electronic Lexical Database''. Cambridge: MIT Press, 305-332.
 
Hirst, G., and St-Onge, D. (1998). [http://mirror.eacoss.org/documentation/ITLibrary/IRIS/Data/1997/Hirst/Lexical/1997-Hirst-Lexical.pdf Lexical chains as representation of context for the detection and correction of malapropisms]. In C. Fellbaum (ed.), ''WordNet: An Electronic Lexical Database''. Cambridge: MIT Press, 305-332.
  
−
Jarmasz, M., and Szpakowicz, S. (2003). [http://www.site.uottawa.ca/~mjarmasz/pubs/jarmasz_roget_sim.pdf Roget’s thesaurus and semantic similarity], ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03)'', Borovets, Bulgaria, September, pp. 212-219.
+
Jarmasz, M., and Szpakowicz, S. (2003). [http://www.csi.uottawa.ca/~szpak/recent_papers/TR-2003-01.pdf Roget’s thesaurus and semantic similarity], ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03)'', Borovets, Bulgaria, September, pp. 212-219.
  
 
Jiang, J.J., and Conrath, D.W. (1997). [http://wortschatz.uni-leipzig.de/~sbordag/aalw05/Referate/03_Assoziationen_BudanitskyResnik/Jiang_Conrath_97.pdf Semantic similarity based on corpus statistics and lexical taxonomy]. ''Proceedings of the International Conference on Research in Computational Linguistics'', Taiwan.
 
Jiang, J.J., and Conrath, D.W. (1997). [http://wortschatz.uni-leipzig.de/~sbordag/aalw05/Referate/03_Assoziationen_BudanitskyResnik/Jiang_Conrath_97.pdf Semantic similarity based on corpus statistics and lexical taxonomy]. ''Proceedings of the International Conference on Research in Computational Linguistics'', Taiwan.
 +
 +
Karlgren, J. and Sahlgren, M. (2001). [http://www.sics.se/~jussi/Artiklar/2001_RWIbook/KarlgrenSahlgren2001.pdf From Words to Understanding]. In Uesaka, Y., Kanerva, P., & Asoh, H. (Eds.), ''Foundations of Real-World Intelligence'', Stanford: CSLI Publications, pp. 294–308.
  
 
Landauer, T.K., and Dumais, S.T. (1997). [http://lsa.colorado.edu/papers/plato/plato.annote.html A solution to Plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge]. ''Psychological Review'', 104(2):211–240.
 
Landauer, T.K., and Dumais, S.T. (1997). [http://lsa.colorado.edu/papers/plato/plato.annote.html A solution to Plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge]. ''Psychological Review'', 104(2):211–240.
  
−
Leacock, C., and Chodorow, M. (1998). Combining local context and WordNet similarity for word sense identification. In C. Fellbaum (ed.), ''WordNet: An Electronic Lexical Database''. Cambridge: MIT Press, pp. 265-283.
+
Leacock, C., and Chodorow, M. (1998). [http://books.google.ca/books?id=Rehu8OOzMIMC&lpg=PA265&ots=IpnaLkZUec&lr&pg=PA265#v=onepage&q&f=false Combining local context and WordNet similarity for word sense identification]. In C. Fellbaum (ed.), ''WordNet: An Electronic Lexical Database''. Cambridge: MIT Press, pp. 265-283.
  
 
Lin, D. (1998). [http://www.cs.ualberta.ca/~lindek/papers/sim.pdf An information-theoretic definition of similarity]. ''Proceedings of the 15th International Conference on Machine Learning (ICML-98)'', Madison, WI, pp. 296-304.
 
Lin, D. (1998). [http://www.cs.ualberta.ca/~lindek/papers/sim.pdf An information-theoretic definition of similarity]. ''Proceedings of the 15th International Conference on Machine Learning (ICML-98)'', Madison, WI, pp. 296-304.
Line 213: Line 317:
 
Matveeva, I., Levow, G., Farahat, A., and Royer, C. (2005). [http://people.cs.uchicago.edu/~matveeva/SynGLSA_ranlp_final.pdf Generalized latent semantic analysis for term representation]. ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-05)'', Borovets, Bulgaria.
 
Matveeva, I., Levow, G., Farahat, A., and Royer, C. (2005). [http://people.cs.uchicago.edu/~matveeva/SynGLSA_ranlp_final.pdf Generalized latent semantic analysis for term representation]. ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-05)'', Borovets, Bulgaria.
  
−
S. Pado and M. Lapata. [http://www.coli.uni-saarland.de/~pado/pub/papers/cl07_pado.pdf Dependency-based construction of semantic space models]. ''Computational Linguistics'', 33(2), 161-199.
+
Pado, S., and Lapata, M. (2007). [http://www.nlpado.de/~sebastian/pub/papers/cl07_pado.pdf Dependency-based construction of semantic space models]. ''Computational Linguistics'', 33(2), 161-199.
 +
 
 +
Pennington, J., Socher, R., and Manning, C. (2014). [https://www.aclweb.org/anthology/D14-1162 Glove: Global vectors for word representation]. ''EMNLP 2014'', pp. 1532-1543.
 +
 
 +
Pilehvar, M.T., Jurgens D., and Navigli R. (2013). [http://wwwusers.di.uniroma1.it/~navigli/pubs/ACL_2013_Pilehvar_Jurgens_Navigli.pdf Align, disambiguate and walk: A unified approach for measuring semantic similarity]. ''Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013),'' Sofia, Bulgaria.
  
 
Rapp, R. (2003). [http://www.amtaweb.org/summit/MTSummit/FinalPapers/19-Rapp-final.pdf Word sense discovery based on sense descriptor dissimilarity]. ''Proceedings of the Ninth Machine Translation Summit'', pp. 315-322.
 
Rapp, R. (2003). [http://www.amtaweb.org/summit/MTSummit/FinalPapers/19-Rapp-final.pdf Word sense discovery based on sense descriptor dissimilarity]. ''Proceedings of the Ninth Machine Translation Summit'', pp. 315-322.
Line 220: Line 328:
  
 
Ruiz-Casado, M., Alfonseca, E. and Castells, P. (2005) [http://alfonseca.org/pubs/2005-ranlp1.pdf Using context-window overlapping in Synonym Discovery and Ontology Extension]. ''Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP-2005)'', Borovets, Bulgaria.
 
Ruiz-Casado, M., Alfonseca, E. and Castells, P. (2005) [http://alfonseca.org/pubs/2005-ranlp1.pdf Using context-window overlapping in Synonym Discovery and Ontology Extension]. ''Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP-2005)'', Borovets, Bulgaria.
 +
 +
Salle A., Idiart M., and Villavicencio A. (2018) [https://github.com/alexandres/lexvec/blob/master/README.md LexVec]
 +
 +
Speer, R., Chin, J., and Havasi, C. (2017). [https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14972/14051 Conceptnet 5.5: An open multilingual graph of general knowledge]. ''AAAI-17'', pp. 4444-4451.
  
 
Terra, E., and Clarke, C.L.A. (2003). [http://acl.ldc.upenn.edu/N/N03/N03-1032.pdf Frequency estimates for statistical word similarity measures]. ''Proceedings of the Human Language Technology and North American Chapter of Association of Computational Linguistics Conference 2003 (HLT/NAACL 2003)'', pp. 244–251.
 
Terra, E., and Clarke, C.L.A. (2003). [http://acl.ldc.upenn.edu/N/N03/N03-1032.pdf Frequency estimates for statistical word similarity measures]. ''Proceedings of the Human Language Technology and North American Chapter of Association of Computational Linguistics Conference 2003 (HLT/NAACL 2003)'', pp. 244–251.
 +
 +
Tsatsaronis, G., Varlamis, I., and Vazirgiannis, M. (2010). [http://arxiv.org/abs/1401.5699 Text Relatedness Based on a Word Thesaurus]. ''Journal of Artificial Intelligence Research'' 37, 1–39
  
 
Turney, P.D. (2001). [http://arxiv.org/abs/cs.LG/0212033 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.
 
Turney, P.D. (2001). [http://arxiv.org/abs/cs.LG/0212033 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.
Line 227: Line 341:
 
Turney, P.D., Littman, M.L., Bigham, J., and Shnayder, V. (2003). [http://arxiv.org/abs/cs.CL/0309035 Combining independent modules to solve multiple-choice synonym and analogy problems]. ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03)'', Borovets, Bulgaria, pp. 482-489.
 
Turney, P.D., Littman, M.L., Bigham, J., and Shnayder, V. (2003). [http://arxiv.org/abs/cs.CL/0309035 Combining independent modules to solve multiple-choice synonym and analogy problems]. ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03)'', Borovets, Bulgaria, pp. 482-489.
  
−
 
+
Turney, P.D. (2008). [http://arxiv.org/abs/0809.0124 A uniform approach to analogies, synonyms, antonyms, and associations]. ''Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)'', Manchester, UK, pp. 905-912.
−
== See also ==
 
−
 
 
−
* [[Attributional and Relational Similarity (State of the art)]]
 
−
* [[SAT Analogy Questions]]
 
−
* [[State of the art]]
 
  
  
 
[[Category:State of the art]]
 
[[Category:State of the art]]
 +
[[Category:Similarity]]

Latest revision as of 17:32, 15 September 2019

  • the TOEFL questions are available on request by contacting LSA Support at CU Boulder, the people who manage the LSA web site at Colorado
  • TOEFL = Test of English as a Foreign Language
  • 80 multiple-choice synonym questions; 4 choices per question
  • introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring degree of similarity between words
  • subsequently used by many other researchers
  • see also: Similarity (State of the art)


Sample question

Stem: levied
Choices: (a) imposed
(b) believed
(c) requested
(d) correlated
Solution: (a) imposed


Table of results

Algorithm Reference for algorithm Reference for experiment Type Correct 95% confidence
RES Resnik (1995) Jarmasz and Szpakowicz (2003) Hybrid 20.31% 12.89–31.83%
LC Leacock and Chodrow (1998) Jarmasz and Szpakowicz (2003) Lexicon-based 21.88% 13.91–33.21%
LIN Lin (1998) Jarmasz and Szpakowicz (2003) Hybrid 24.06% 15.99–35.94%
Random Random guessing 1 / 4 = 25.00% Random 25.00% 15.99–35.94%
JC Jiang and Conrath (1997) Jarmasz and Szpakowicz (2003) Hybrid 25.00% 15.99–35.94%
LSA Landauer and Dumais (1997) Landauer and Dumais (1997) Corpus-based 64.38% 52.90–74.80%
Human Average non-English US college applicant Landauer and Dumais (1997) Human 64.50% 53.01–74.88%
RI Karlgren and Sahlgren (2001) Karlgren and Sahlgren (2001) Corpus-based 72.50% 61.38-81.90%
DS Pado and Lapata (2007) Pado and Lapata (2007) Corpus-based 73.00% 62.72-82.96%
PMI-IR Turney (2001) Turney (2001) Corpus-based 73.75% 62.72–82.96%
PairClass Turney (2008) Turney (2008) Corpus-based 76.25% 65.42-85.06%
HSO Hirst and St.-Onge (1998) Jarmasz and Szpakowicz (2003) Lexicon-based 77.91% 68.17–87.11%
JS Jarmasz and Szpakowicz (2003) Jarmasz and Szpakowicz (2003) Lexicon-based 78.75% 68.17–87.11%
Sa18 Salle et al. (2018) Dobó (2019) Corpus-based 80.00% 69.56–88.11%
PMI-IR Terra and Clarke (2003) Terra and Clarke (2003) Corpus-based 81.25% 70.97–89.11%
LC-IR Higgins (2005) Higgins (2005) Web-based 81.25% 70.97–89.11%
Do19-corpus Dobó (2019) Dobó (2019) Corpus-based 81.25% 70.97–89.11%
CWO Ruiz-Casado et al. (2005) Ruiz-Casado et al. (2005) Web-based 82.55% 72.38–90.09%
PPMIC Bullinaria and Levy (2007) Bullinaria and Levy (2007) Corpus-based 85.00% 75.26-92.00%
GLSA Matveeva et al. (2005) Matveeva et al. (2005) Corpus-based 86.25% 76.73-92.93%
SR Tsatsaronis et al. (2010) Tsatsaronis et al. (2010) Lexicon-based 87.50% 78.21-93.84%
DC13 Dobó and Csirik (2013) Dobó and Csirik (2013) Corpus-based 88.75% 79.72-94.72%
Pe14 Pennington et al. (2014) Dobó (2019) Corpus-based 90.00% 81.24-95.58%
LSA Rapp (2003) Rapp (2003) Corpus-based 92.50% 84.39-97.20%
LSA Han (2014) Han (2014) Hybrid 95.0% 87.69-98.62%
ADW Pilehvar et al. (2013) Pilehvar et al. (2013) WordNet graph-based (unsupervised) 96.25% 89.43-99.22%
PR Turney et al. (2003) Turney et al. (2003) Hybrid 97.50% 91.26–99.70%
Sp19 Speer et al. (2017) Dobó (2019) Hybrid 98.75% 93.23–99.97%
Do19-hybrid Dobó (2019) Dobó (2019) Hybrid 98.75% 93.23–99.97%
PCCP Bullinaria and Levy (2012) Bullinaria and Levy (2012) Corpus-based 100.00% 96.32-100.00%

Explanation of table

  • Algorithm = name of algorithm
  • Reference for algorithm = where to find out more about given algorithm
  • Reference for experiment = where to find out more about evaluation of given algorithm with TOEFL questions
  • Type = general type of algorithm: corpus-based, lexicon-based, hybrid
  • Correct = percent of 80 questions that given algorithm answered correctly
  • 95% confidence = confidence interval calculated using the Binomial Exact Test
  • table rows sorted in order of increasing percent correct
  • several WordNet-based similarity measures are implemented in Ted Pedersen's WordNet::Similarity package
  • LSA = Latent Semantic Analysis
  • PCCP = Principal Component vectors with Caron P
  • PMI-IR = Pointwise Mutual Information - Information Retrieval
  • PR = Product Rule
  • PPMIC = Positive Pointwise Mutual Information with Cosine
  • GLSA = Generalized Latent Semantic Analysis
  • CWO = Context Window Overlapping
  • DS = Dependency Space
  • RI = Random Indexing

Notes

  • the performance of a corpus-based algorithm depends on the corpus, so the difference in performance between two corpus-based systems may be due to the different corpora, rather than the different algorithms
  • the TOEFL questions include nouns, verbs, and adjectives, but some of the WordNet-based algorithms were only designed to work with nouns; this explains some of the lower scores
  • some of the algorithms may have been tuned on the TOEFL questions; read the references for details
  • Landauer and Dumais (1997) report scores that were corrected for guessing by subtracting a penalty of 1/3 for each incorrect answer; they report a score of 52.5% when this penalty is applied; when the penalty is removed, their performance is 64.4% correct

References

Bullinaria, J.A., and Levy, J.P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39(3), 510-526.

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