Difference between revisions of "SAT Analogy Questions (State of the art)"
| Line 15: | Line 15: | ||
* LRA = Latent Relational Analysis | * LRA = Latent Relational Analysis | ||
* PERT = Pertinence | * PERT = Pertinence | ||
| + | * PMI-IR = Pointwise Mutual Information - Information Retrieval | ||
| Line 67: | Line 68: | ||
| 33.2% | | 33.2% | ||
| 28.7-38.5% | | 28.7-38.5% | ||
| + | |- | ||
| + | | PMI-IR | ||
| + | | Turney (2001) | ||
| + | | Turney (2006b) | ||
| + | | Corpus-based | ||
| + | | 35.0% | ||
| + | | 30.2-40.1% | ||
|- | |- | ||
| KNOW-BEST | | KNOW-BEST | ||
| Line 111: | Line 119: | ||
Turney, P.D., and Littman, M.L. (2005). [http://arxiv.org/abs/cs.LG/0508103 Corpus-based learning of analogies and semantic relations]. ''Machine Learning'', 60 (1-3), 251-278. | Turney, P.D., and Littman, M.L. (2005). [http://arxiv.org/abs/cs.LG/0508103 Corpus-based learning of analogies and semantic relations]. ''Machine Learning'', 60 (1-3), 251-278. | ||
| + | |||
| + | 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. (2006a). [http://arxiv.org/abs/cs.CL/0607120 Expressing implicit semantic relations without supervision]. ''Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (Coling/ACL-06)'', Sydney, Australia, pp. 313-320. | Turney, P.D. (2006a). [http://arxiv.org/abs/cs.CL/0607120 Expressing implicit semantic relations without supervision]. ''Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (Coling/ACL-06)'', Sydney, Australia, pp. 313-320. | ||
Revision as of 06:25, 13 May 2007
- SAT = Scholastic Aptitude Test
- 374 multiple-choice analogy questions; 5 choices per question
- SAT questions collected by Michael Littman, available from Peter Turney
- introduced in Turney et al. (2003) as a way of evaluating algorithms for measuring relational similarity
- 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 SAT questions
- Type = general type of algorithm: corpus-based, lexicon-based, hybrid
- Correct = percent of 374 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
- KNOW-BEST = KNOWledge-Based Entertainment and Scholastic Testing
- VSM = Vector Space Model
- LRA = Latent Relational Analysis
- PERT = Pertinence
- PMI-IR = Pointwise Mutual Information - Information Retrieval
| Algorithm | Reference for algorithm | Reference for experiment | Type | Correct | 95% confidence |
|---|---|---|---|---|---|
| Random | Random guessing | 1 / 5 = 20.0% | Random | 20.0% | 16.1-24.5% |
| JC | Jiang and Conrath (1997) | Turney (2006b) | Hybrid | 27.3% | 23.1-32.4% |
| LIN | Lin (1998) | Turney (2006b) | Hybrid | 27.3% | 23.1-32.4% |
| LC | Leacock and Chodrow (1998) | Turney (2006b) | Lexicon-based | 31.3% | 26.9-36.5% |
| HSO | Hirst and St.-Onge (1998) | Turney (2006b) | Lexicon-based | 32.1% | 27.6-37.4% |
| RES | Resnik (1995) | Turney (2006b) | Hybrid | 33.2% | 28.7-38.5% |
| PMI-IR | Turney (2001) | Turney (2006b) | Corpus-based | 35.0% | 30.2-40.1% |
| KNOW-BEST | Veale (2004) | Veale (2004) | Lexicon-based | 43.0% | 38.0-48.2% |
| VSM | Turney and Littman (2005) | Turney and Littman (2005) | Corpus-based | 47.1% | 42.2-52.5% |
| PERT | Turney (2006a) | Turney (2006a) | Corpus-based | 53.5% | 48.5-58.9% |
| LRA | Turney (2006b) | Turney (2006b) | Corpus-based | 56.1% | 51.0–61.2% |
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.
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.
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.
Turney, P.D., and Littman, M.L. (2005). Corpus-based learning of analogies and semantic relations. Machine Learning, 60 (1-3), 251-278.
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. (2006a). Expressing implicit semantic relations without supervision. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (Coling/ACL-06), Sydney, Australia, pp. 313-320.
Turney, P.D. (2006b). Similarity of semantic relations. Computational Linguistics, 32 (3), 379-416.
Veale, T. (2004). WordNet sits the SAT: A knowledge-based approach to lexical analogy. Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 606–612, Valencia, Spain.