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%)
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
| Algorithm | Reference | Description | Accuracy | F |
|---|---|---|---|---|
| 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% |
| QKC | Qiu et al. (2006) | supervised sentence dissimilarity classification | 72.0% | 81.6% |
| WDDP | Wan et al. (2006) | supervised dependency-based features | 75.6% | 83.0% |
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.
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.
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.