Difference between revisions of "RG-65 Test Collection (State of the art)"
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* The similarity values in the dataset are the means of judgments made by 51 subjects [Rubenstein and Goodenough, 1965]. | * 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)]] | * see also: [[Similarity (State of the art)]] | ||
| + | |||
== Table of results == | == Table of results == | ||
| + | |||
| + | * '''Listed in order of decreasing [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rho].''' | ||
| + | |||
{| border="1" cellpadding="5" cellspacing="1" width="100%" | {| border="1" cellpadding="5" cellspacing="1" width="100%" | ||
| Line 13: | Line 17: | ||
! Reference for reported results | ! Reference for reported results | ||
! Type | ! Type | ||
| − | ! Spearman correlation (ρ) | + | ! [http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient Spearman correlation] (ρ) |
| − | ! Pearson correlation (r) | + | ! [http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient Pearson correlation] (r) |
| + | |- | ||
| + | | ADW | ||
| + | | Pilehvar and Navigli (2015) | ||
| + | | Pilehvar and Navigli (2015) | ||
| + | | Knowledge-based (Wiktionary) | ||
| + | | 0.920 | ||
| + | | 0.910 | ||
| + | |- | ||
| + | | Sp17 | ||
| + | | Speer et al. (2017) | ||
| + | | Dobó (2019) | ||
| + | | Hybrid | ||
| + | | 0.901 | ||
| + | | 0.896 | ||
| + | |- | ||
| + | | Do19-hybrid | ||
| + | | Dobó (2019) | ||
| + | | Dobó (2019) | ||
| + | | Hybrid | ||
| + | | 0.899 | ||
| + | | 0.914 | ||
| + | |- | ||
| + | | Y&Q | ||
| + | | Yih and Qazvinian (2012) | ||
| + | | Yih and Qazvinian (2012) | ||
| + | | Hybrid | ||
| + | | 0.890 | ||
| + | | - | ||
| + | |- | ||
| + | | NASARI | ||
| + | | Camacho-Collados et al. (2015) | ||
| + | | Camacho-Collados et al. (2015) | ||
| + | | Hybrid | ||
| + | | 0.880 | ||
| + | | 0.910 | ||
|- | |- | ||
| ADW | | ADW | ||
| Pilehvar et al. (2013) | | Pilehvar et al. (2013) | ||
| Pilehvar et al. (2013) | | Pilehvar et al. (2013) | ||
| − | | Knowledge-based | + | | Knowledge-based (WordNet) |
| 0.868 | | 0.868 | ||
| 0.810 | | 0.810 | ||
| Line 29: | Line 68: | ||
| 0.838 | | 0.838 | ||
| - | | - | ||
| + | |- | ||
| + | | SSA | ||
| + | | Hassan and Mihalcea (2011) | ||
| + | | Hassan and Mihalcea (2011) | ||
| + | | Corpus-based | ||
| + | | 0.833 | ||
| + | | 0.861 | ||
|- | |- | ||
| PPR | | PPR | ||
| Line 78: | Line 124: | ||
| 0.788 | | 0.788 | ||
| 0.834 | | 0.834 | ||
| + | |- | ||
| + | | Pe14 | ||
| + | | Pennington et al. (2014) | ||
| + | | Dobó (2019) | ||
| + | | Corpus-based | ||
| + | | 0.769 | ||
| + | | 0.770 | ||
| + | |- | ||
| + | | Sa18 | ||
| + | | Salle et al. (2018) | ||
| + | | Dobó (2019) | ||
| + | | Corpus-based | ||
| + | | 0.763 | ||
| + | | 0.792 | ||
|- | |- | ||
| ESA* | | ESA* | ||
| Line 92: | Line 152: | ||
| 0.741 | | 0.741 | ||
| 0.729 | | 0.729 | ||
| + | |- | ||
| + | | Do19-corpus | ||
| + | | Dobó (2019) | ||
| + | | Dobó (2019) | ||
| + | | Corpus-based | ||
| + | | 0.732 | ||
| + | | 0.737 | ||
|- | |- | ||
| Resnik | | Resnik | ||
| Line 126: | Line 193: | ||
== References == | == References == | ||
| − | * | + | * '''Listed alphabetically.''' |
| + | |||
| + | |||
| + | Agirre, Eneko, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca, Aitor Soroa: [http://www.aclweb.org/anthology/N09-1003 A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches]. HLT-NAACL 2009: 19-27 | ||
| + | |||
| + | Camacho-Collados, José, Pilehvar, Mohammad Taher, and Navigli, Roberto: [http://aclweb.org/anthology/N/N15/N15-1059.pdf NASARI: a Novel Approach to a Semantically-Aware Representation of Items]. NAACL 2015, pp. 567-577, Denver, USA. | ||
| + | |||
| + | Dobó, A. (2019). [http://doktori.bibl.u-szeged.hu/10120/1/AndrasDoboThesis2019.pdf A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages]. University of Szeged. [https://github.com/doboandras/dsm-parameter-analysis GitHub repository] | ||
| + | |||
| + | Gabrilovich, Evgeniy, and Shaul Markovitch, [http://www.cs.technion.ac.il/~gabr/papers/ijcai-2007-sim.pdf 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: [http://www.cse.unt.edu/~rada/papers/hassan.aaai11.pdf 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. [http://www.site.uottawa.ca/~mdislam/publications/LREC_06_242.pdf 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. [http://www.arxiv.org/pdf/1204.0140 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. | |
| − | + | Pennington, J., Socher, R., and Manning, C. (2014). [https://www.aclweb.org/anthology/D14-1162 Glove: Global vectors for word representation]. ''EMNLP 2014'', pp. 1532-1543. | |
| − | + | Pilehvar, M.T., Jurgens, D. and Navigli, R. [http://wwwusers.di.uniroma1.it/~navigli/pubs/ACL_2013_Pilehvar_Jurgens_Navigli.pdf 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. | |
| − | + | Pilehvar, M.T. and Navigli, R. [http://www.sciencedirect.com/science/article/pii/S000437021500106X From Senses to Texts: An All-in-one Graph-based Approach for Measuring Semantic Similarity]. Artificial Intelligence, Elsevier. | |
| − | + | 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. | |
| − | + | Salle A., Idiart M., and Villavicencio A. (2018) [https://github.com/alexandres/lexvec/blob/master/README.md LexVec] | |
| − | + | Speer, R., Chin, J., and Havasi, C. (2017). [https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14972/14051 Conceptnet 5.5: An open multilingual graph of general knowledge]. ''AAAI-17'', pp. 4444-4451. | |
| − | + | Strube, Michael, Simone Paolo Ponzetto: WikiRelate! Computing Semantic Relatedness Using Wikipedia. AAAI 2006: 1419-1424 | |
| + | Yih, W. and Qazvinian, V. (2012). [http://aclweb.org/anthology/N/N12/N12-1077.pdf 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). | ||
[[Category:State of the art]] | [[Category:State of the art]] | ||
| + | [[Category:Similarity]] | ||
Latest revision as of 18:33, 15 September 2019
- 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) |
|---|---|---|---|---|---|
| ADW | Pilehvar and Navigli (2015) | Pilehvar and Navigli (2015) | Knowledge-based (Wiktionary) | 0.920 | 0.910 |
| Sp17 | Speer et al. (2017) | Dobó (2019) | Hybrid | 0.901 | 0.896 |
| Do19-hybrid | Dobó (2019) | Dobó (2019) | Hybrid | 0.899 | 0.914 |
| Y&Q | Yih and Qazvinian (2012) | Yih and Qazvinian (2012) | Hybrid | 0.890 | - |
| NASARI | Camacho-Collados et al. (2015) | Camacho-Collados et al. (2015) | Hybrid | 0.880 | 0.910 |
| ADW | Pilehvar et al. (2013) | Pilehvar et al. (2013) | Knowledge-based (WordNet) | 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 |
| Pe14 | Pennington et al. (2014) | Dobó (2019) | Corpus-based | 0.769 | 0.770 |
| Sa18 | Salle et al. (2018) | Dobó (2019) | Corpus-based | 0.763 | 0.792 |
| 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 |
| Do19-corpus | Dobó (2019) | Dobó (2019) | Corpus-based | 0.732 | 0.737 |
| 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
Camacho-Collados, José, Pilehvar, Mohammad Taher, and Navigli, Roberto: NASARI: a Novel Approach to a Semantically-Aware Representation of Items. NAACL 2015, pp. 567-577, Denver, USA.
Dobó, A. (2019). A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages. University of Szeged. GitHub repository
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.
Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. EMNLP 2014, pp. 1532-1543.
Pilehvar, M.T., Jurgens, D. and Navigli, R. 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.
Pilehvar, M.T. and Navigli, R. From Senses to Texts: An All-in-one Graph-based Approach for Measuring Semantic Similarity. Artificial Intelligence, Elsevier.
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.
Salle A., Idiart M., and Villavicencio A. (2018) LexVec
Speer, R., Chin, J., and Havasi, C. (2017). Conceptnet 5.5: An open multilingual graph of general knowledge. AAAI-17, pp. 4444-4451.
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).