TOEFL Synonym Questions (State of the art)

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* introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring similarity
 
* introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring similarity
 
* subsequently used by many other researchers
 
* subsequently used by many other researchers
 +
* '''Algorithm''' = name of algorithm
 
* '''Reference for algorithm''' = where to find out more about given algorithm for measuring similarity
 
* '''Reference for algorithm''' = where to find out more about given algorithm for measuring similarity
 
* '''Reference for experiment''' = where to find out more about evaluation of given algorithm with TOEFL questions
 
* '''Reference for experiment''' = where to find out more about evaluation of given algorithm with TOEFL questions
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* 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
 +
* PMI-IR = Pointwise Mutual Information - Information Retrieval
 +
* PR = Product Rule
  
  
 
{| border="1" cellpadding="5" cellspacing="1" width="100%"
 
{| border="1" cellpadding="5" cellspacing="1" width="100%"
 
|-
 
|-
 +
! Algorithm
 
! Reference for algorithm
 
! Reference for algorithm
 
! Reference for experiment
 
! Reference for experiment
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! 95% confidence
 
! 95% confidence
 
|-
 
|-
 +
| RES
 
| Resnik (1995)
 
| Resnik (1995)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 12.89–31.83%
 
| 12.89–31.83%
 
|-
 
|-
 +
| LC
 
| Leacock and Chodrow (1998)
 
| Leacock and Chodrow (1998)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 13.91–33.21%
 
| 13.91–33.21%
 
|-
 
|-
 +
| LIN
 
| Lin (1998)
 
| Lin (1998)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 15.99–35.94%
 
| 15.99–35.94%
 
|-
 
|-
 +
| JC
 
| Jiang and Conrath (1997)
 
| Jiang and Conrath (1997)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 15.99–35.94%
 
| 15.99–35.94%
 
|-
 
|-
 +
| LSA
 
| Landauer and Dumais (1997)
 
| Landauer and Dumais (1997)
 
| Landauer and Dumais (1997)
 
| Landauer and Dumais (1997)
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| 52.90–74.80%
 
| 52.90–74.80%
 
|-
 
|-
 +
|
 
| Average non-English US college applicant
 
| Average non-English US college applicant
 
| Landauer and Dumais (1997)
 
| Landauer and Dumais (1997)
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| 53.01–74.88%
 
| 53.01–74.88%
 
|-
 
|-
 +
| PMI-IR
 
| Turney (2001)
 
| Turney (2001)
 
| Turney (2001)
 
| Turney (2001)
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| 62.71–82.96%
 
| 62.71–82.96%
 
|-
 
|-
 +
| HSO
 
| Hirst and St.-Onge (1998)
 
| Hirst and St.-Onge (1998)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 68.17–87.11%
 
| 68.17–87.11%
 
|-
 
|-
 +
| JS
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
 
| Jarmasz and Szpakowicz (2003)
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| 68.17–87.11%
 
| 68.17–87.11%
 
|-
 
|-
 +
| PMI-IR
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
 
| Terra and Clarke (2003)
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| 70.97–89.11%
 
| 70.97–89.11%
 
|-
 
|-
 +
| RAP
 
| Rapp (2003)
 
| Rapp (2003)
 
| Rapp (2003)
 
| Rapp (2003)
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| 84.39-97.20%
 
| 84.39-97.20%
 
|-
 
|-
 +
| PR
 
| Turney et al. (2003)
 
| Turney et al. (2003)
 
| Turney et al. (2003)
 
| Turney et al. (2003)

Revision as of 09:52, 12 May 2007

  • TOEFL = Test of English as a Foreign Language
  • 80 multiple-choice synonym questions; 4 choices per question
  • TOEFL questions available from Thomas Landauer
  • introduced in Landauer and Dumais (1997) as a way of evaluating algorithms for measuring similarity
  • subsequently used by many other researchers
  • Algorithm = name of algorithm
  • Reference for algorithm = where to find out more about given algorithm for measuring similarity
  • Reference for experiment = where to find out more about evaluation of given algorithm with TOEFL questions
  • Algorithm = 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 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
  • PMI-IR = Pointwise Mutual Information - Information Retrieval
  • PR = Product Rule


Algorithm Reference for algorithm Reference for experiment Algorithm 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%
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%
Average non-English US college applicant Landauer and Dumais (1997) human 64.50% 53.01–74.88%
PMI-IR Turney (2001) Turney (2001) corpus-based 73.75% 62.71–82.96%
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%
PMI-IR Terra and Clarke (2003) Terra and Clarke (2003) corpus-based 81.25% 70.97–89.11%
RAP Rapp (2003) Rapp (2003) corpus-based 92.50% 84.39-97.20%
PR Turney et al. (2003) Turney et al. (2003) hybrid 97.50% 91.26–99.70%


Hirst, G., and St-Onge, D. (1998). 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). 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). Semantic similarity based on corpus statistics and lexical taxonomy. Proceedings of the International Conference on Research in Computational Linguistics, Taiwan.

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.

Lin, D. (1998). An information-theoretic definition of similarity. Proceedings of the 15th International Conference on Machine Learning (ICML-98), Madison, WI, pp. 296-304.

Resnik, P. (1995). Using information content to evaluate semantic similarity. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, pp. 448-453.

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