Difference between revisions of "SAT Analogy Questions (State of the art)"
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* introduced in Turney et al. (2003) as a way of evaluating algorithms for measuring relational similarity | * introduced in Turney et al. (2003) as a way of evaluating algorithms for measuring relational similarity | ||
* '''Algorithm''' = name of algorithm | * '''Algorithm''' = name of algorithm | ||
− | * '''Reference for algorithm''' = where to find out more about given 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 | * '''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 | * '''Type''' = general type of algorithm: corpus-based, lexicon-based, hybrid |
Revision as of 05:32, 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
- VSM = Vector Space Model
- LRA = Latent Relational Analysis
Algorithm | Reference for algorithm | Reference for experiment | Type | Correct | 95% confidence |
---|---|---|---|---|---|
HSO | Hirst and St.-Onge (1998) | Turney (2006b) | Lexicon-based | 32.1% | 27.6-37.4% |
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.
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. (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.