ACL Wiki - User contributions [en]

WordSimilarity-353 Test Collection (State of the art)

2014-01-28T23:28:32Z

Shassan: /* References */

* [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ 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 [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ here]
* performance is measured by [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rank correlation coefficient]
* introduced by [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Finkelstein et al. (2002)]
* subsequently used by many other researchers
* see also: [[Similarity (State of the art)]]

== Table of results ==

* '''Listed in order of increasing [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rho].'''

{| border="1" cellpadding="5" cellspacing="1"
|-
! Algorithm
! Reference for algorithm
! Reference for reported results
! Type
! Spearman's rho
! Pearson's r
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| WNE
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.305
| 0.271
|-
| J&C
| Jiang and Conrath 1997
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.318
| 0.354
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.348
| 0.341
|-
| H&S
| Hirst and St-Onge (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| Lin
| Lin (1998)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.348
| 0.357
|-
| Resnik
| Resnik (1995)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.353
| 0.365
|-
| ROGET
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.415
| 0.536
|-
| C&W
| Collobert and Weston (2008)
| Collobert and Weston (2008)
| Corpus-based
| 0.5
| N/A
|-
| WikiRelate
| Strube and Ponzetto (2006)
| Strube and Ponzetto (2006)
| Corpus-based
| N/A
| 0.48
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.492
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.563
|-
| simVB+simWN
| Finkelstein et al. (2002)
| Finkelstein et al. (2002)
| Hybrid
| N/A
| 0.55
|-
| SSA
| Hassan and Mihalcea (2011)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.622
| 0.629
|-
| HSMN+csmRNN
| Luong et al. (2013)
| Luong et al. (2013)
| Corpus-based
| 0.65
| N/A
|-
| Multi-prototype
| Huang et al. (2012)
| Huang et al. (2012)
| Corpus-based
| 0.71
| N/A
|-
| Multi-lingual SSA
| Hassan et al. (2011)
| Hassan et al. (2011)
| Corpus-based
| 0.713
| 0.674
|-
| ESA
| Gabrilovich and Markovitch (2007)
| Gabrilovich and Markovitch (2007)
| Corpus-based
| 0.748
| 0.503
|-
| TSA
| Radinsky et al. (2011)
| Radinsky et al. (2011)
| Hybrid
| 0.80
| N/A
|-
| CLEAR
| Halawi et al. (2012)
| Halawi et al. (2012)
| Corpus-based
| 0.81
| N/A
|-
| Y&Q
| Yih and Qazvinian (2012)
| Yih and Qazvinian (2012)
| Hybrid
| 0.81
| N/A
|-
|}

== References ==

Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. (2002) [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Placing Search in Context: The Concept Revisited]. ACM Transactions on Information Systems, 20(1):116-131.

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.samerhassan.com/images/4/48/Hassan.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.

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.

Pilehvar, M.T., D. Jurgens and R. Navigli. [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.

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.

Halawi, Guy, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. (2012). [http://gabrilovich.com/publications/papers/Halawi2012LSL.pdf Large-scale learning of word relatedness with constraints]. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1406-1414. ACM.

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.

Radinsky, Kira, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. (2011). [http://gabrilovich.com/publications/papers/Radinsky2011WTS.pdf A word at a time: computing word relatedness using temporal semantic analysis]. In Proceedings of the 20th international conference on World wide web, pp. 337-346. ACM.

Strube, Michael and Simone Paolo Ponzetto. (2006). [http://www.aaai.org/Papers/AAAI/2006/AAAI06-223.pdf WikiRelate! Computing Semantic Relatedness Using Wikipedia]. Proceedings of The 21st National Conference on Artificial Intelligence (AAAI), Boston, MA.

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]]

WordSimilarity-353 Test Collection (State of the art)

2014-01-08T02:18:29Z

Shassan: /* Table of results */

* [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ 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 [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ here]
* performance is measured by [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rank correlation coefficient]
* introduced by [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Finkelstein et al. (2002)]
* subsequently used by many other researchers
* see also: [[Similarity (State of the art)]]

== Table of results ==

* '''Listed in order of increasing [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rho].'''

{| border="1" cellpadding="5" cellspacing="1"
|-
! Algorithm
! Reference for algorithm
! Reference for reported results
! Type
! Spearman's rho
! Pearson's r
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| WNE
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.305
| 0.271
|-
| J&C
| Jiang and Conrath 1997
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.318
| 0.354
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.348
| 0.341
|-
| H&S
| Hirst and St-Onge (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| Lin
| Lin (1998)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.348
| 0.357
|-
| Resnik
| Resnik (1995)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.353
| 0.365
|-
| ROGET
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.415
| 0.536
|-
| C&W
| Collobert and Weston (2008)
| Collobert and Weston (2008)
| Corpus-based
| 0.5
| N/A
|-
| WikiRelate
| Strube and Ponzetto (2006)
| Strube and Ponzetto (2006)
| Corpus-based
| N/A
| 0.48
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.492
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.563
|-
| simVB+simWN
| Finkelstein et al. (2002)
| Finkelstein et al. (2002)
| Hybrid
| N/A
| 0.55
|-
| SSA
| Hassan and Mihalcea (2011)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.622
| 0.629
|-
| HSMN+csmRNN
| Luong et al. (2013)
| Luong et al. (2013)
| Corpus-based
| 0.65
| N/A
|-
| Multi-prototype
| Huang et al. (2012)
| Huang et al. (2012)
| Corpus-based
| 0.71
| N/A
|-
| Multi-lingual SSA
| Hassan et al. (2011)
| Hassan et al. (2011)
| Corpus-based
| 0.713
| 0.674
|-
| ESA
| Gabrilovich and Markovitch (2007)
| Gabrilovich and Markovitch (2007)
| Corpus-based
| 0.748
| 0.503
|-
| TSA
| Radinsky et al. (2011)
| Radinsky et al. (2011)
| Hybrid
| 0.80
| N/A
|-
| CLEAR
| Halawi et al. (2012)
| Halawi et al. (2012)
| Corpus-based
| 0.81
| N/A
|-
|}

== References ==

Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. (2002) [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Placing Search in Context: The Concept Revisited]. ACM Transactions on Information Systems, 20(1):116-131.

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.

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.

Pilehvar, M.T., D. Jurgens and R. Navigli. [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.

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.

Halawi, Guy, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. (2012). [http://gabrilovich.com/publications/papers/Halawi2012LSL.pdf Large-scale learning of word relatedness with constraints]. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1406-1414. ACM.

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.

Radinsky, Kira, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. (2011). [http://gabrilovich.com/publications/papers/Radinsky2011WTS.pdf A word at a time: computing word relatedness using temporal semantic analysis]. In Proceedings of the 20th international conference on World wide web, pp. 337-346. ACM.

Strube, Michael and Simone Paolo Ponzetto. (2006). [http://www.aaai.org/Papers/AAAI/2006/AAAI06-223.pdf WikiRelate! Computing Semantic Relatedness Using Wikipedia]. Proceedings of The 21st National Conference on Artificial Intelligence (AAAI), Boston, MA.

[[Category:State of the art]]

WordSimilarity-353 Test Collection (State of the art)

2014-01-08T02:17:31Z

Shassan: /* References */

* [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ 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 [http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ here]
* performance is measured by [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rank correlation coefficient]
* introduced by [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Finkelstein et al. (2002)]
* subsequently used by many other researchers
* see also: [[Similarity (State of the art)]]

== Table of results ==

* '''Listed in order of increasing [http://en.wikipedia.org/wiki/Spearman_rank_correlation Spearman's rho].'''

{| border="1" cellpadding="5" cellspacing="1"
|-
! Algorithm
! Reference for algorithm
! Reference for reported results
! Type
! Spearman's rho
! Pearson's r
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| WNE
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.305
| 0.271
|-
| J&C
| Jiang and Conrath 1997
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.318
| 0.354
|-
| L&C
| Leacock and Chodorow (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.348
| 0.341
|-
| H&S
| Hirst and St-Onge (1998)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.302
| 0.356
|-
| Lin
| Lin (1998)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.348
| 0.357
|-
| Resnik
| Resnik (1995)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.353
| 0.365
|-
| ROGET
| Jarmasz (2003)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.415
| 0.536
|-
| C&W
| Collobert and Weston (2008)
| Collobert and Weston (2008)
| Corpus-based
| 0.5
| N/A
|-
| WikiRelate
| Strube and Ponzetto (2006)
| Strube and Ponzetto (2006)
| Corpus-based
| N/A
| 0.48
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.492
|-
| LSA
| Landauer et al. (1997)
| Hassan and Mihalcea (2011)
| Corpus-based
| 0.581
| 0.563
|-
| simVB+simWN
| Finkelstein et al. (2002)
| Finkelstein et al. (2002)
| Hybrid
| N/A
| 0.55
|-
| SSA
| Hassan and Mihalcea (2011)
| Hassan and Mihalcea (2011)
| Knowledge-based
| 0.622
| 0.629
|-
| HSMN+csmRNN
| Luong et al. (2013)
| Luong et al. (2013)
| Corpus-based
| 0.65
| N/A
|-
| Multi-prototype
| Huang et al. (2012)
| Huang et al. (2012)
| Corpus-based
| 0.71
| N/A
|-
| Multi-lingual SSA
| Hassan et al. (2011)
| Hassan et al. (2011)
| Corpus-based
| 0.713
| 0.674
|-
| ESA
| Gabrilovich and Markovitch (2007)
| Gabrilovich and Markovitch (2007)
| Corpus-based
| 0.748
| 0.503
|-
| TSA
| Radinsky et al. (2011)
| Radinsky et al. (2011)
| Hybrid
| 0.80
| N/A
|-
| CLEAR
| Halawi et al. (2012)
| Halawi et al. (2012)
| Corpus-based
| 0.81
| N/A
|-
}

== References ==

Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. (2002) [http://www.cs.technion.ac.il/~gabr/papers/tois_context.pdf Placing Search in Context: The Concept Revisited]. ACM Transactions on Information Systems, 20(1):116-131.

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.

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.

Pilehvar, M.T., D. Jurgens and R. Navigli. [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.

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.

Halawi, Guy, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. (2012). [http://gabrilovich.com/publications/papers/Halawi2012LSL.pdf Large-scale learning of word relatedness with constraints]. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1406-1414. ACM.

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.

Radinsky, Kira, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. (2011). [http://gabrilovich.com/publications/papers/Radinsky2011WTS.pdf A word at a time: computing word relatedness using temporal semantic analysis]. In Proceedings of the 20th international conference on World wide web, pp. 337-346. ACM.

Strube, Michael and Simone Paolo Ponzetto. (2006). [http://www.aaai.org/Papers/AAAI/2006/AAAI06-223.pdf WikiRelate! Computing Semantic Relatedness Using Wikipedia]. Proceedings of The 21st National Conference on Artificial Intelligence (AAAI), Boston, MA.

[[Category:State of the art]]

WordSimilarity-353 Test Collection (State of the art)

2014-01-08T02:11:26Z

Shassan: /* Table of results */

Paraphrase Identification (State of the art)

2013-12-24T23:15:44Z

Shassan: /* References */

* '''source''': [http://research.microsoft.com/en-us/downloads/607D14D9-20CD-47E3-85BC-A2F65CD28042/default.aspx Microsoft Research Paraphrase Corpus] (MSRP)
* '''task''': given a pair of sentences, classify them as paraphrases or not paraphrases
* '''see''': Dolan et al. (2004)
* '''train''': 4,076 sentence pairs (2,753 positive: 67.5%)
* '''test''': 1,725 sentence pairs (1,147 positive: 66.5%)
* '''see also:''' [[Similarity (State of the art)]]

== Sample data ==

* '''Sentence 1''': Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.
* '''Sentence 2''': Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.
* '''Class''': 1 (true paraphrase)

== Table of results ==

* '''Listed in order of increasing F score.'''

{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! Algorithm
! Reference
! Description
! Supervision
! Accuracy
! F
|-
| Vector Based Similarity (Baseline)
| Mihalcea et al. (2006)
| cosine similarity with tf-idf weighting
| unsupervised
| 65.4%
| 75.3%
|-
| ESA
| Hassan (2011)
| explicit semantic space
| unsupervised
| 67.0%
| 79.3%
|-
| KM
| Kozareva and Montoyo (2006)
| combination of lexical and semantic features
| supervised
| 76.6%
| 79.6%
|-
| LSA
| Hassan (2011)
| latent semantic space
| unsupervised
| 68.8%
| 79.9%
|-
| RMLMG
| Rus et al. (2008)
| graph subsumption
| unsupervised
| 70.6%
| 80.5%
|-
| MCS
| Mihalcea et al. (2006)
| combination of several word similarity measures
| unsupervised
| 70.3%
| 81.3%
|-
| STS
| Islam and Inkpen (2007)
| combination of semantic and string similarity
| unsupervised
| 72.6%
| 81.3%
|-
| SSA
| Hassan (2011)
| salient semantic space
| unsupervised
| 72.5%
| 81.4%
|-
| QKC
| Qiu et al. (2006)
| sentence dissimilarity classification
| supervised
| 72.0%
| 81.6%
|-
| ParaDetect
| Zia and Wasif (2012)
| PI using semantic heuristic features
| supervised
| 74.7%
| 81.8%
|-
| SDS
| Blacoe and Lapata (2012)
| simple distributional semantic space
| supervised
| 73.0%
| 82.3%
|-
| matrixJcn
| Fernando and Stevenson (2008)
| JCN WordNet similarity with matrix
| unsupervised
| 74.1%
| 82.4%
|-
| FHS
| Finch et al. (2005)
| combination of MT evaluation measures as features
| supervised
| 75.0%
| 82.7%
|-
| PE
| Das and Smith (2009)
| product of experts
| supervised
| 76.1%
| 82.7%
|-
| WDDP
| Wan et al. (2006)
| dependency-based features
| supervised
| 75.6%
| 83.0%
|-
| SHPNM
| Socher et al. (2011)
| recursive autoencoder with dynamic pooling
| supervised
| 76.8%
| 83.6%
|-
| MTMETRICS
| Madnani et al. (2012)
| combination of eight machine translation metrics
| supervised
| 77.4%
| 84.1%
|-
|}

== References ==

* '''Listed alphabetically.'''

Blacoe, W. and Lapata, M. (2012). [http://newdesign.aclweb.org/anthology/D/D12/D12-1050.pdf A comparison of vector-based representations for semantic composition], ''Proceedings of EMNLP'', Jeju Island, Korea, pp. 546-556.

Das, D., and Smith, N. (2009). [http://www.aclweb.org/anthology-new/P/P09/P09-1053.pdf Paraphrase identification as probabilistic quasi-synchronous recognition]. ''Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP'', pp. 468-476, Suntec, Singapore.

Dolan, B., Quirk, C., and Brockett, C. (2004). [http://acl.ldc.upenn.edu/C/C04/C04-1051.pdf Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources], ''Proceedings of the 20th international conference on Computational Linguistics (COLING 2004)'', Geneva, Switzerland, pp. 350-356.

Fernando, S., and Stevenson, M. (2008). [http://www.dcs.shef.ac.uk/~samf/clukPaper.pdf A semantic similarity approach to paraphrase detection], ''Computational Linguistics UK (CLUK 2008) 11th Annual Research Colloquium''.

Finch, A., and H, Y.S., and Sumita, E. (2005). [http://aclweb.org/anthology/I/I05/I05-5003.pdf Using machine translation evaluation techniques to determine sentence-level semantic equivalence], "Proceedings of the Third International Workshop on Paraphrasing (IWP 2005)", Jeju Island, South Korea, pp. 17-24.

Hassan, Samer. [http://samerhassan.com/images/0/01/Dissertation.pdf Measuring Semantic Relatedness Using Salient Encyclopedic Concepts]. Doctor of Philosophy, August 2011

Islam, A., and Inkpen, D. (2007). [http://www.site.uottawa.ca/~diana/publications/ranlp_2007_textsim_camera_ready.pdf Semantic similarity of short texts], ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2007)'', Borovets, Bulgaria, pp. 291-297.

Kozareva, Z., and Montoyo, A. (2006). [http://www.dlsi.ua.es/~zkozareva/papers/fintalKozareva.pdf Paraphrase identification on the basis of supervised machine learning techniques], ''Advances in Natural Language Processing: 5th International Conference on NLP (FinTAL 2006)'', Turku, Finland, 524-533.

Madnani, N., Tetreault, J., and Chodorow, M. (2012). [http://www.aclweb.org/anthology-new/N/N12/N12-1019.pdf Re-examining Machine Translation Metrics for Paraphrase Identification], ''Proceedings of 2012 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2012)'', pp. 182-190.

Mihalcea, R., Corley, C., and Strapparava, C. (2006). [http://www.cse.unt.edu/~rada/papers/mihalcea.aaai06.pdf Corpus-based and knowledge-based measures of text semantic similarity], ''Proceedings of the National Conference on Artificial Intelligence (AAAI 2006)'', Boston, Massachusetts, pp. 775-780.

Qiu, L. and Kan, M.Y. and Chua, T.S. (2006). [http://acl.ldc.upenn.edu/W/W06/W06-1603.pdf Paraphrase recognition via dissimilarity significance classification], ''Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006)'', pp. 18-26.

Rus, V. and McCarthy, P.M. and Lintean, M.C. and McNamara, D.S. and Graesser, A.C. (2008). [http://csep.psyc.memphis.edu/McNamara/pdf/Paraphrase_Identification.pdf Paraphrase identification with lexico-syntactic graph subsumption], ''FLAIRS 2008'', pp. 201-206.

Socher, R. and Huang, E.H., and Pennington, J. and Ng, A.Y., and Manning, C.D. (2011). [http://www.socher.org/uploads/Main/SocherHuangPenningtonNgManning_NIPS2011.pdf Dynamic pooling and unfolding recursive autoencoders for paraphrase detection], "Advances in Neural Information Processing Systems 24"

Wan, S., Dras, M., Dale, R., and Paris, C. (2006). [http://www.alta.asn.au/events/altw2006/proceedings/swan-final.pdf Using dependency-based features to take the "para-farce" out of paraphrase], ''Proceedings of the Australasian Language Technology Workshop (ALTW 2006)'', pp. 131-138.

Zia Ul-Qayyum and Wasif Altaf, (2012). [http://maxwellsci.com/print/rjaset/v4-4894-4904.pdf Paraphrase Identification using Semantic Heuristic Features], ''Research Journal of Applied Sciences, Engineering and Technology'', 4(22): 4894-4904.



[[Category:State of the art]]

Paraphrase Identification (State of the art)

2013-12-24T23:12:17Z

Shassan: /* Table of results */

* '''source''': [http://research.microsoft.com/en-us/downloads/607D14D9-20CD-47E3-85BC-A2F65CD28042/default.aspx Microsoft Research Paraphrase Corpus] (MSRP)
* '''task''': given a pair of sentences, classify them as paraphrases or not paraphrases
* '''see''': Dolan et al. (2004)
* '''train''': 4,076 sentence pairs (2,753 positive: 67.5%)
* '''test''': 1,725 sentence pairs (1,147 positive: 66.5%)
* '''see also:''' [[Similarity (State of the art)]]

== Sample data ==

* '''Sentence 1''': Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.
* '''Sentence 2''': Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.
* '''Class''': 1 (true paraphrase)

== Table of results ==

* '''Listed in order of increasing F score.'''

{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! Algorithm
! Reference
! Description
! Supervision
! Accuracy
! F
|-
| Vector Based Similarity (Baseline)
| Mihalcea et al. (2006)
| cosine similarity with tf-idf weighting
| unsupervised
| 65.4%
| 75.3%
|-
| ESA
| Hassan (2011)
| explicit semantic space
| unsupervised
| 67.0%
| 79.3%
|-
| KM
| Kozareva and Montoyo (2006)
| combination of lexical and semantic features
| supervised
| 76.6%
| 79.6%
|-
| LSA
| Hassan (2011)
| latent semantic space
| unsupervised
| 68.8%
| 79.9%
|-
| RMLMG
| Rus et al. (2008)
| graph subsumption
| unsupervised
| 70.6%
| 80.5%
|-
| MCS
| Mihalcea et al. (2006)
| combination of several word similarity measures
| unsupervised
| 70.3%
| 81.3%
|-
| STS
| Islam and Inkpen (2007)
| combination of semantic and string similarity
| unsupervised
| 72.6%
| 81.3%
|-
| SSA
| Hassan (2011)
| salient semantic space
| unsupervised
| 72.5%
| 81.4%
|-
| QKC
| Qiu et al. (2006)
| sentence dissimilarity classification
| supervised
| 72.0%
| 81.6%
|-
| ParaDetect
| Zia and Wasif (2012)
| PI using semantic heuristic features
| supervised
| 74.7%
| 81.8%
|-
| SDS
| Blacoe and Lapata (2012)
| simple distributional semantic space
| supervised
| 73.0%
| 82.3%
|-
| matrixJcn
| Fernando and Stevenson (2008)
| JCN WordNet similarity with matrix
| unsupervised
| 74.1%
| 82.4%
|-
| FHS
| Finch et al. (2005)
| combination of MT evaluation measures as features
| supervised
| 75.0%
| 82.7%
|-
| PE
| Das and Smith (2009)
| product of experts
| supervised
| 76.1%
| 82.7%
|-
| WDDP
| Wan et al. (2006)
| dependency-based features
| supervised
| 75.6%
| 83.0%
|-
| SHPNM
| Socher et al. (2011)
| recursive autoencoder with dynamic pooling
| supervised
| 76.8%
| 83.6%
|-
| MTMETRICS
| Madnani et al. (2012)
| combination of eight machine translation metrics
| supervised
| 77.4%
| 84.1%
|-
|}

== References ==

* '''Listed alphabetically.'''

Blacoe, W. and Lapata, M. (2012). [http://newdesign.aclweb.org/anthology/D/D12/D12-1050.pdf A comparison of vector-based representations for semantic composition], ''Proceedings of EMNLP'', Jeju Island, Korea, pp. 546-556.

Das, D., and Smith, N. (2009). [http://www.aclweb.org/anthology-new/P/P09/P09-1053.pdf Paraphrase identification as probabilistic quasi-synchronous recognition]. ''Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP'', pp. 468-476, Suntec, Singapore.

Dolan, B., Quirk, C., and Brockett, C. (2004). [http://acl.ldc.upenn.edu/C/C04/C04-1051.pdf Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources], ''Proceedings of the 20th international conference on Computational Linguistics (COLING 2004)'', Geneva, Switzerland, pp. 350-356.

Fernando, S., and Stevenson, M. (2008). [http://www.dcs.shef.ac.uk/~samf/clukPaper.pdf A semantic similarity approach to paraphrase detection], ''Computational Linguistics UK (CLUK 2008) 11th Annual Research Colloquium''.

Finch, A., and H, Y.S., and Sumita, E. (2005). [http://aclweb.org/anthology/I/I05/I05-5003.pdf Using machine translation evaluation techniques to determine sentence-level semantic equivalence], "Proceedings of the Third International Workshop on Paraphrasing (IWP 2005)", Jeju Island, South Korea, pp. 17-24.

Islam, A., and Inkpen, D. (2007). [http://www.site.uottawa.ca/~diana/publications/ranlp_2007_textsim_camera_ready.pdf Semantic similarity of short texts], ''Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2007)'', Borovets, Bulgaria, pp. 291-297.

Kozareva, Z., and Montoyo, A. (2006). [http://www.dlsi.ua.es/~zkozareva/papers/fintalKozareva.pdf Paraphrase identification on the basis of supervised machine learning techniques], ''Advances in Natural Language Processing: 5th International Conference on NLP (FinTAL 2006)'', Turku, Finland, 524-533.

Madnani, N., Tetreault, J., and Chodorow, M. (2012). [http://www.aclweb.org/anthology-new/N/N12/N12-1019.pdf Re-examining Machine Translation Metrics for Paraphrase Identification], ''Proceedings of 2012 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2012)'', pp. 182-190.

Mihalcea, R., Corley, C., and Strapparava, C. (2006). [http://www.cse.unt.edu/~rada/papers/mihalcea.aaai06.pdf Corpus-based and knowledge-based measures of text semantic similarity], ''Proceedings of the National Conference on Artificial Intelligence (AAAI 2006)'', Boston, Massachusetts, pp. 775-780.

Qiu, L. and Kan, M.Y. and Chua, T.S. (2006). [http://acl.ldc.upenn.edu/W/W06/W06-1603.pdf Paraphrase recognition via dissimilarity significance classification], ''Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006)'', pp. 18-26.

Rus, V. and McCarthy, P.M. and Lintean, M.C. and McNamara, D.S. and Graesser, A.C. (2008). [http://csep.psyc.memphis.edu/McNamara/pdf/Paraphrase_Identification.pdf Paraphrase identification with lexico-syntactic graph subsumption], ''FLAIRS 2008'', pp. 201-206.

Socher, R. and Huang, E.H., and Pennington, J. and Ng, A.Y., and Manning, C.D. (2011). [http://www.socher.org/uploads/Main/SocherHuangPenningtonNgManning_NIPS2011.pdf Dynamic pooling and unfolding recursive autoencoders for paraphrase detection], "Advances in Neural Information Processing Systems 24"

Wan, S., Dras, M., Dale, R., and Paris, C. (2006). [http://www.alta.asn.au/events/altw2006/proceedings/swan-final.pdf Using dependency-based features to take the "para-farce" out of paraphrase], ''Proceedings of the Australasian Language Technology Workshop (ALTW 2006)'', pp. 131-138.

Zia Ul-Qayyum and Wasif Altaf, (2012). [http://maxwellsci.com/print/rjaset/v4-4894-4904.pdf Paraphrase Identification using Semantic Heuristic Features], ''Research Journal of Applied Sciences, Engineering and Technology'', 4(22): 4894-4904.



[[Category:State of the art]]