Difference between revisions of "Paraphrase Identification (State of the art)"

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== Table of results ==
 
== Table of results ==
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'''Listed in order of increasing F score.'''
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| FHS
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| Vector Based Similarity (Baseline)
| Finch et al. (2005)
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| Mihalcea et al. (2006), Fernando and Stevenson (2008)
| supervised combination of MT evaluation measures as features
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| Cosine similarity with tf-idf weighting
| 75.0%
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| 65.4%
| 82.7%
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| 75.3%
 
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| KM
 
| KM
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| 74.1%
 
| 74.1%
 
| 82.4%
 
| 82.4%
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| FHS
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| Finch et al. (2005)
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| supervised combination of MT evaluation measures as features
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| 75.0%
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| 82.7%
 
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| WDDP
 
| WDDP
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| 84.1%
 
| 84.1%
 
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| Vector Based Similarity (Baseline)
 
| Mihalcea et al. (2006), Fernando and Stevenson (2008)
 
| Cosine similarity with tf-idf weighting
 
| 65.4%
 
| 75.3%
 
 
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Revision as of 10:44, 26 September 2012

  • source: 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%)


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.


Algorithm Reference Description Accuracy F
Vector Based Similarity (Baseline) Mihalcea et al. (2006), Fernando and Stevenson (2008) Cosine similarity with tf-idf weighting 65.4% 75.3%
KM Kozareva and Montoyo (2006) supervised combination of lexical and semantic features 76.6% 79.6%
RMLMG Rus et al. (2008) unsupervised graph subsumption 70.6% 80.5%
MCS Mihalcea et al. (2006) unsupervised combination of several word similarity measures 70.3% 81.3%
STS Islam and Inkpen (2007) unsupervised combination of semantic and string similarity 72.6% 81.3%
QKC Qiu et al. (2006) supervised sentence dissimilarity classification 72.0% 81.6%
matrixJcn Fernando and Stevenson (2008) unsupervised JCN WordNet similarity with matrix 74.1% 82.4%
FHS Finch et al. (2005) supervised combination of MT evaluation measures as features 75.0% 82.7%
WDDP Wan et al. (2006) supervised dependency-based features 75.6% 83.0%
SHPNM Socher et al. (2011) supervised recursive autoencoder with dynamic pooling 76.8% 83.6%
MTMETRICS Madnani et al. (2012) supervised combination of eight machine translation metrics 77.4% 84.1%

References

Dolan, B., Quirk, C., and Brockett, C. (2004). 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). 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). 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). 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). 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). 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). 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). 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). 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). 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). Using dependency-based features to take the "para-farce" out of paraphrase, Proceedings of the Australasian Language Technology Workshop (ALTW 2006), pp. 131-138.

See also