Difference between revisions of "Similar-Associated-Both Test Collection (State of the art)"
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* Chiarello et al. (1990) used the dataset in human priming experiments; they did not measure classification accuracy | * Chiarello et al. (1990) used the dataset in human priming experiments; they did not measure classification accuracy | ||
* see also: [[Similarity (State of the art)]] | * see also: [[Similarity (State of the art)]] | ||
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== Table of results == | == Table of results == | ||
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| 70.1-84.3% | | 70.1-84.3% | ||
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== Notes == | == Notes == | ||
* '''95% confidence''' = confidence interval calculated using the [[Statistical calculators|Binomial Exact Test]] | * '''95% confidence''' = confidence interval calculated using the [[Statistical calculators|Binomial Exact Test]] | ||
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== References == | == References == | ||
Revision as of 12:15, 28 June 2015
- the contrast between taxonomical similarity (co-hyponymy) and association (co-occurrence)
- 144 word pairs that they labeled similar-only, associated-only, or similar+associated
- 48 pairs in each of the three classes
- test collection created by Chiarello et al. (1990)
- Chiarello et al. (1990) used the dataset in human priming experiments; they did not measure classification accuracy
- see also: Similarity (State of the art)
Table of results
| Algorithm | Reference | Accuracy | 95% confidence |
|---|---|---|---|
| Dual-Space | Turney (2012) | 61.1% | 52.6-69.1% |
| PairClass | Turney (2008) | 77.1% | 70.1-84.3% |
Notes
- 95% confidence = confidence interval calculated using the Binomial Exact Test
References
Chiarello, C., Burgess, C., Richards, L., & Pollock, A. (1990). Semantic and associative priming in the cerebral hemispheres: Some words do, some words don't . . . sometimes, some places. Brain and Language, 38, 75{104.
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.