RG-65 Test Collection (State of the art)
- state of the art in Rubenstein & Goodenough (RG-65) dataset
- 65 word pairs;
- Similarity of each pair is scored according to a scale from 0 to 4 (the higher the "similarity of meaning," the higher the number);
- The similarity values in the dataset are the means of judgments made by 51 subjects [Rubenstein and Goodenough, 1965].
- see also: Similarity (State of the art)
Table of results
- Listed in order of decreasing Spearman's rho.
| Algorithm | Reference for algorithm | Reference for reported results | Type | Spearman correlation (ρ) | Pearson correlation (r) |
|---|---|---|---|---|---|
| Y&Q | Yih and Qazvinian (2012) | Yih and Qazvinian (2012) | Hybrid | 0.890 | - |
| ADW | Pilehvar et al. (2013) | Pilehvar et al. (2013) | Knowledge-based | 0.868 | 0.810 |
| PPR | Hughes and Ramage (2007) | Hughes and Ramage (2007) | Knowledge-based | 0.838 | - |
| SSA | Hassan and Mihalcea (2011) | Hassan and Mihalcea (2011) | Corpus-based | 0.833 | 0.861 |
| PPR | Agirre et al. (2009) | Agirre et al. (2009) | Knowledge-based | 0.830 | - |
| H&S | Hirst and St-Onge (1998) | Hassan and Mihalcea (2011) | Knowledge-based | 0.813 | 0.732 |
| Roget | Jarmasz (2003) | Hassan and Mihalcea (2011) | Knowledge-based | 0.804 | 0.818 |
| J&C | Jiang and Conrath (1997) | Hassan and Mihalcea (2011) | Knowledge-based | 0.804 | 0.731 |
| WNE | Jarmasz (2003) | Hassan and Mihalcea (2011) | Knowledge-based | 0.801 | 0.787 |
| L&C | Leacock and Chodorow (1998) | Hassan and Mihalcea (2011) | Knowledge-based | 0.797 | 0.852 |
| Lin | Lin (1998) | Hassan and Mihalcea (2011) | Corpus-based | 0.788 | 0.834 |
| ESA* | Gabrilovich and Markovitch (2007) | Hassan and Mihalcea (2011) | Corpus-based | 0.749 | 0.716 |
| SOCPMI* | Islam and Inkpen (2006) | Hassan and Mihalcea (2011) | Corpus-based | 0.741 | 0.729 |
| Resnik | Resnik (1995) | Hassan and Mihalcea (2011) | Knowledge-based | 0.731 | 0.800 |
| WLM | Milne and Witten (2008) | Milne and Witten (2008) | Knowledge-based | 0.640 | - |
| LSA* | Landauer et al. (1997) | Hassan and Mihalcea (2011) | Corpus-based | 0.609 | 0.644 |
| WikiRelate | Strube and Ponzetto (2006) | Strube and Ponzetto (2006) | Knowledge-based | - | 0.530 |
Note: values reported by (Hassan and Mihalcea, 2011) are "based on the collected raw data from the respective authors", and those highlighted by (*) are re-implementations.
References
- Listed alphabetically.
Agirre, Eneko, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca, Aitor Soroa: A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches. HLT-NAACL 2009: 19-27
Gabrilovich, Evgeniy, and Shaul Markovitch, Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis, Proceedings of The 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, 2007.
Hassan, Samer, and Rada Mihalcea: Semantic Relatedness Using Salient Semantic Analysis. AAAI 2011
Hirst, Graeme and David St-Onge. Lexical chains as representations of context for the detection and correction of malapropisms. In Christiane Fellbaum, editor, WordNet: An Electronic Lexical Database. The MIT Press, Cambridge, MA, pages 305–332, 1998.
Hughes, Thad, Daniel Ramage, Lexical Semantic Relatedness with Random Graph Walks. EMNLP-CoNLL 2007: 581-589.
Islam, A., and Inkpen, D. 2006. Second order co-occurrence pmi for determining the semantic similarity of words. Proceedings of the International Conference on Language Resources and Evaluation (LREC 2006) 1033–1038.
Jarmasz, M. 2003. Roget’s thesaurus as a Lexical Resource for Natural Language Processing. Ph.D. Dissertation, Ottawa Carleton Institute for Computer Science, School of Information Technology and Engineering, University of Ottawa.
Jiang, Jay J. and David W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics (ROCLING X), Taiwan, pages 19–33, 1997.
Landauer, T. K.; L, T. K.; Laham, D.; Rehder, B.; and Schreiner, M. E. 1997. How well can passage meaning be derived without using word order? a comparison of latent semantic analysis and humans.
Leacock, Claudia and Martin Chodorow. Combining local context and WordNet similarity for word sense identification. In Christiane Fellbaum, editor, WordNet: An Electronic Lexical Database. The MIT Press, Cambridge, MA, pages 265–283, 1998.
Lin, Dekang. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning, Madison,WI, pages 296–304, 1998.
Milne, David, and Ian H. Witten, An Effective, Low-Cost Measure of Semantic Relatedness Obtained from Wikipedia Links, In Proceedings of AAAI 2008.
Pilehvar, M.T., D. Jurgens and R. Navigli. Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4-9, 2013, pp. 1341-1351.
Resnik, Philip. Using information content to evaluate semantic similarity. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448–453, Montreal, Canada, 1995.
Rubenstein, Herbert, and John B. Goodenough. Contextual correlates of synonymy. Communications of the ACM, 8(10):627–633, 1965.
Strube, Michael, Simone Paolo Ponzetto: WikiRelate! Computing Semantic Relatedness Using Wikipedia. AAAI 2006: 1419-1424
Yih, W. and Qazvinian, V. (2012). Measuring Word Relatedness Using Heterogeneous Vector Space Models. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012).