TOEFL Synonym Questions (State of the art)
- 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 degree of similarity between two words
- subsequently used by many other researchers
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% |
| 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% |
| PPMIC | Bullinaria and Levy (2006) | Bullinaria and Levy (2006) | 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% |
| LSA | 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% |
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 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
- PPMIC = Positive Pointwise Mutual Information with Cosine
- GLSA = Generalized Latent Semantic Analysis
References
Bullinaria, J.A., and Levy, J.P. (2006). Extracting semantic representations from word co-occurrence statistics: A computational study. To appear in Behavior Research Methods, 38.
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.
Landauer, T.K., and Dumais, S.T. (1997). 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.
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.
Matveeva, I., Levow, G., Farahat, A., and Royer, C. (2005). Generalized latent semantic analysis for term representation. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-05), Borovets, Bulgaria.
Rapp, R. (2003). Word sense discovery based on sense descriptor dissimilarity, Proceedings of the Ninth Machine Translation Summit, pp. 315-322.
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.
Terra, E., and Clarke, C.L.A. (2003). 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.
Turney, P.D. (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.
Turney, P.D., Littman, M.L., Bigham, J., and Shnayder, V. (2003). 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.