Paraphrase Identification (State of the art)

• 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%)
• 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.

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%
SAMS-RecNN	Cheng and Kartsaklis (2015)	Recursive NNs using syntax-aware multi-sense word embeddings	supervised	78.6%	85.3%
TF-KLD	Ji and Eisenstein (2013)	Matrix factorization with supervised reweighting	supervised	80.4%	85.9%

References

• Listed alphabetically.

Blacoe, W. and Lapata, M. (2012). A comparison of vector-based representations for semantic composition, Proceedings of EMNLP, Jeju Island, Korea, pp. 546-556.

Cheng, J. and Kartsaklis, D. (2015). Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning, Proceedings of EMNLP 2015, Lisbon, Portugal, pp. 1531-1542.

Das, D., and Smith, N. (2009). 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). 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.

Hassan, Samer. Measuring Semantic Relatedness Using Salient Encyclopedic Concepts. Doctor of Philosophy, August 2011

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.

Ji, Y. and Eisenstein, J. (2013) Discriminative Improvements to Distributional Sentence Similarity, Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, Washington, USA, pp. 891--896

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.

Zia Ul-Qayyum and Wasif Altaf, (2012). Paraphrase Identification using Semantic Heuristic Features, Research Journal of Applied Sciences, Engineering and Technology, 4(22): 4894-4904.