Difference between revisions of "SAT Analogy Questions (State of the art)"
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* SAT questions collected by [http://www.cs.rutgers.edu/~mlittman/ Michael Littman], available on request from [http://www.apperceptual.com/ Peter Turney] | * SAT questions collected by [http://www.cs.rutgers.edu/~mlittman/ Michael Littman], available on request from [http://www.apperceptual.com/ Peter Turney] | ||
* 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 | ||
| + | * see also: [[Similarity (State of the art)]], [[Analogy (State of the art)]] | ||
| Line 133: | Line 134: | ||
| 47.1% | | 47.1% | ||
| 42.2-52.5% | | 42.2-52.5% | ||
| + | |- | ||
| + | | Dual-Space | ||
| + | | Turney (2012) | ||
| + | | Turney (2012) | ||
| + | | Corpus-based | ||
| + | | 51.1% | ||
| + | | 46.1-56.5% | ||
|- | |- | ||
| BMI | | BMI | ||
| Line 154: | Line 162: | ||
| 53.5% | | 53.5% | ||
| 48.5-58.9% | | 48.5-58.9% | ||
| + | |- | ||
| + | | SuperSim | ||
| + | | Turney (2013) | ||
| + | | Turney (2013) | ||
| + | | Corpus-based | ||
| + | | 54.8% | ||
| + | | 49.6-59.9% | ||
| + | |- | ||
| + | | ConceptNet | ||
| + | | Speer et al. (2017) | ||
| + | | Speer et al. (2017) | ||
| + | | Hybrid | ||
| + | | 56.1% | ||
| + | | 51.0-61.2% | ||
|- | |- | ||
| LRA | | LRA | ||
| Line 160: | Line 182: | ||
| Corpus-based | | Corpus-based | ||
| 56.1% | | 56.1% | ||
| − | | 51. | + | | 51.0-61.2% |
|- | |- | ||
| Human | | Human | ||
| Line 169: | Line 191: | ||
| 52.0-62.3% | | 52.0-62.3% | ||
|- | |- | ||
| + | | Human Voting | ||
| + | | Lofi (2013) | ||
| + | | Lofi (2013) | ||
| + | | Human Voting | ||
| + | | 81.5% | ||
| + | | 77.2-85.4% | ||
|} | |} | ||
| Line 178: | Line 206: | ||
* '''Type''' = general type of algorithm: corpus-based, lexicon-based, hybrid | * '''Type''' = general type of algorithm: corpus-based, lexicon-based, hybrid | ||
* '''Correct''' = percent of 374 questions that given algorithm answered correctly | * '''Correct''' = percent of 374 questions that given algorithm answered correctly | ||
| − | * '''95% confidence''' = confidence interval calculated using [ | + | * '''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. | + | * 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 |
* KNOW-BEST = KNOWledge-Based Entertainment and Scholastic Testing | * KNOW-BEST = KNOWledge-Based Entertainment and Scholastic Testing | ||
* VSM = Vector Space Model | * VSM = Vector Space Model | ||
| Line 188: | Line 216: | ||
* LSA+Predication = Latent Semantic Analysis + Predication | * LSA+Predication = Latent Semantic Analysis + Predication | ||
* BagPack = Bag of words representation of Paired concept knowledge | * BagPack = Bag of words representation of Paired concept knowledge | ||
| + | * ConceptNet = ConceptNet Numberbatch 2016.09 | ||
== References == | == References == | ||
| Line 201: | Line 230: | ||
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. | ||
| − | 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. | ||
| − | Mangalath, P., Quesada, J., and Kintsch, W. (2004). [http://www. | + | Lofi, C. (2013). [http://www.ifis.cs.tu-bs.de/sites/default/files/biblio/13cdim_final_pdf_80985.pdf Just ask a human?--Controlling Quality in Relational Similarity and Analogy Processing using the Crowd]. ''Proceedings of the Workshop of the 15th BTW Conference on Database Systems for Business, Technology, and Web (BTW 2013)'', Magdeburg, Germany, pp. 197-210. |
| + | |||
| + | Mangalath, P., Quesada, J., and Kintsch, W. (2004). [http://www.cogsci.northwestern.edu/cogsci2004/ma/ma355.pdf Analogy-making as predication using relational information and LSA vectors]. In K.D. Forbus, D. Gentner & T. Regier (Eds.), ''Proceedings of the 26th Annual Meeting of the Cognitive Science Society''. Chicago: Lawrence Erlbaum Associates. | ||
Resnik, P. (1995). [http://citeseer.ist.psu.edu/resnik95using.html Using information content to evaluate semantic similarity]. ''Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95)'', Montreal, pp. 448-453. | Resnik, P. (1995). [http://citeseer.ist.psu.edu/resnik95using.html Using information content to evaluate semantic similarity]. ''Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95)'', Montreal, pp. 448-453. | ||
| + | |||
| + | Speer, R., Chin, J., and Havasi, C. (2017). [http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972 ConceptNet 5.5: An Open Multilingual Graph of General Knowledge]. ''Proceedings of The 31st AAAI Conference on Artificial Intelligence'', San Francisco, CA. | ||
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. | ||
| Line 221: | Line 254: | ||
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. | 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. | ||
| − | + | Turney, P.D. (2012). [http://jair.org/papers/paper3640.html Domain and function: A dual-space model of semantic relations and compositions], ''Journal of Artificial Intelligence Research (JAIR)'', 44, 533-585. | |
| − | + | Turney, P.D. (2013), [http://aclweb.org/anthology/Q/Q13/Q13-1029.pdf Distributional semantics beyond words: Supervised learning of analogy and paraphrase], ''Transactions of the Association for Computational Linguistics (TACL)'', 1, 353-366. | |
| − | + | Veale, T. (2004). [http://afflatus.ucd.ie/Papers/ecai2004.pdf 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. | |
| − | |||
| − | |||
| − | |||
[[Category:State of the art]] | [[Category:State of the art]] | ||
| + | [[Category:Similarity]] | ||
| + | [[Category:Analogy]] | ||
Latest revision as of 09:03, 22 March 2017
- SAT = Scholastic Aptitude Test
- 374 multiple-choice analogy questions; 5 choices per question
- SAT questions collected by Michael Littman, available on request from Peter Turney
- introduced in Turney et al. (2003) as a way of evaluating algorithms for measuring relational similarity
- see also: Similarity (State of the art), Analogy (State of the art)
Sample question
Stem: mason:stone Choices: (a) teacher:chalk (b) carpenter:wood (c) soldier:gun (d) photograph:camera (e) book:word Solution: (b) carpenter:wood
Table of results
| 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% |
| LSA+Predication | Mangalath et al. (2004) | Mangalath et al. (2004) | Corpus-based | 42.0% | 37.2-47.4% |
| KNOW-BEST | Veale (2004) | Veale (2004) | Lexicon-based | 43.0% | 38.0-48.2% |
| k-means | Bicici and Yuret (2006) | Bicici and Yuret (2006) | Corpus-based | 44.0% | 39.0-49.3% |
| BagPack | Herdağdelen and Baroni (2009) | Herdağdelen and Baroni (2009) | Corpus-based | 44.1% | 39.0-49.3% |
| VSM | Turney and Littman (2005) | Turney and Littman (2005) | Corpus-based | 47.1% | 42.2-52.5% |
| Dual-Space | Turney (2012) | Turney (2012) | Corpus-based | 51.1% | 46.1-56.5% |
| BMI | Bollegala et al. (2009) | Bollegala et al. (2009) | Corpus-based | 51.1% | 46.1-56.5% |
| PairClass | Turney (2008) | Turney (2008) | Corpus-based | 52.1% | 46.9-57.3% |
| PERT | Turney (2006a) | Turney (2006a) | Corpus-based | 53.5% | 48.5-58.9% |
| SuperSim | Turney (2013) | Turney (2013) | Corpus-based | 54.8% | 49.6-59.9% |
| ConceptNet | Speer et al. (2017) | Speer et al. (2017) | Hybrid | 56.1% | 51.0-61.2% |
| LRA | Turney (2006b) | Turney (2006b) | Corpus-based | 56.1% | 51.0-61.2% |
| Human | Average US college applicant | Turney and Littman (2005) | Human | 57.0% | 52.0-62.3% |
| Human Voting | Lofi (2013) | Lofi (2013) | Human Voting | 81.5% | 77.2-85.4% |
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 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 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
- 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
- LSA+Predication = Latent Semantic Analysis + Predication
- BagPack = Bag of words representation of Paired concept knowledge
- ConceptNet = ConceptNet Numberbatch 2016.09
References
Bicici, E., and Yuret, D. (2006). Clustering word pairs to answer analogy questions. Proceedings of the Fifteenth Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN 2006).
Bollegala D., Matsuo Y., and Ishizuka M. (2009). Measuring the similarity between implicit semantic relations from the web. Proceedings of the 18th International Conference on World Wide Web, ACM, pages 651–660.
Herdağdelen A. and Baroni M. (2009) BagPack: A general framework to represent semantic relations. Proceedings of the EACL 2009 Geometrical Models for Natural Language Semantics (GEMS) Workshop, East Stroudsburg PA: ACL, 33-40.
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.
Lofi, C. (2013). Just ask a human?--Controlling Quality in Relational Similarity and Analogy Processing using the Crowd. Proceedings of the Workshop of the 15th BTW Conference on Database Systems for Business, Technology, and Web (BTW 2013), Magdeburg, Germany, pp. 197-210.
Mangalath, P., Quesada, J., and Kintsch, W. (2004). Analogy-making as predication using relational information and LSA vectors. In K.D. Forbus, D. Gentner & T. Regier (Eds.), Proceedings of the 26th Annual Meeting of the Cognitive Science Society. Chicago: Lawrence Erlbaum Associates.
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.
Speer, R., Chin, J., and Havasi, C. (2017). ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. Proceedings of The 31st AAAI Conference on Artificial Intelligence, San Francisco, CA.
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.
Turney, P.D. (2008). 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.
Turney, P.D. (2012). Domain and function: A dual-space model of semantic relations and compositions, Journal of Artificial Intelligence Research (JAIR), 44, 533-585.
Turney, P.D. (2013), Distributional semantics beyond words: Supervised learning of analogy and paraphrase, Transactions of the Association for Computational Linguistics (TACL), 1, 353-366.
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.