Difference between revisions of "WordSimilarity-353 Test Collection (State of the art)"
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0.65 Spearman rank correlation | 0.65 Spearman rank correlation | ||
http://nlp.stanford.edu/~lmthang/data/papers/conll13_morpho.pdf | http://nlp.stanford.edu/~lmthang/data/papers/conll13_morpho.pdf | ||
| + | |||
| + | |||
| + | == Table of results == | ||
| + | |||
| + | {| border="1" cellpadding="5" cellspacing="1" | ||
| + | |- | ||
| + | ! Algorithm | ||
| + | ! Reference | ||
| + | ! Spearman's rho | ||
| + | |- | ||
| + | | HSMN+csmRNN | ||
| + | | Luong et al. (2013) | ||
| + | | 0.646 | ||
| + | |} | ||
| + | |||
| + | |||
| + | == References == | ||
| + | |||
| + | Luong, Minh-Thang, Richard Socher, and Christopher D. Manning. (2013) [http://nlp.stanford.edu/~lmthang/data/papers/conll13_morpho.pdf Better word representations with recursive neural networks for morphology]. CoNLL-2013: 104. | ||
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| + | |||
| + | == See also == | ||
| + | |||
| + | * [[Attributional and Relational Similarity (State of the art)]] | ||
| + | * [[ESL Synonym Questions (State of the art)|ESL Synonym Questions]] | ||
| + | * [[SAT Analogy Questions]] | ||
| + | * [[State of the art]] | ||
| + | |||
| + | |||
| + | [[Category:State of the art]] | ||
Revision as of 12:39, 3 October 2013
- WordSimilarity-353 Test Collection
- contains two sets of English word pairs along with human-assigned similarity judgements
- first set (set1) contains 153 word pairs along with their similarity scores assigned by 13 subjects
- second set (set2) contains 200 word pairs with similarity assessed by 16 subjects
- WordSimilarity-353 dataset is available here
- performance is measured by Spearman's rank correlation coefficient
- introduced by Finkelstein et al. (2002)
- subsequently used by many other researchers
0.75 Spearman rank correlation
http://www.cs.technion.ac.il/~gabr/papers/ijcai-2007-sim.pdf
0.65 Spearman rank correlation http://nlp.stanford.edu/~lmthang/data/papers/conll13_morpho.pdf
Table of results
| Algorithm | Reference | Spearman's rho |
|---|---|---|
| HSMN+csmRNN | Luong et al. (2013) | 0.646 |
References
Luong, Minh-Thang, Richard Socher, and Christopher D. Manning. (2013) Better word representations with recursive neural networks for morphology. CoNLL-2013: 104.