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

From ACL Wiki
Jump to: navigation, search
Line 1: Line 1:
 
* [http://research.microsoft.com/en-us/downloads/607D14D9-20CD-47E3-85BC-A2F65CD28042/default.aspx Microsoft Research Paraphrase Corpus] (MSRP)
 
* [http://research.microsoft.com/en-us/downloads/607D14D9-20CD-47E3-85BC-A2F65CD28042/default.aspx Microsoft Research Paraphrase Corpus] (MSRP)
 
* see Dolan, Quirk, and Brockett (2004)
 
* see Dolan, Quirk, and Brockett (2004)
* train: 4076 sentence pairs (2753 positive: 67.5%)
+
* train: 4,076 sentence pairs (2,753 positive: 67.5%)
* test: 1725 sentence pairs (1147 positive: 66.5%)
+
* test: 1,725 sentence pairs (1,147 positive: 66.5%)
  
  
Line 24: Line 24:
 
| MCS
 
| MCS
 
| Mihalcea et al. (2006)
 
| Mihalcea et al. (2006)
| combination of several word similarity measures
+
| unsupervised combination of several word similarity measures
 
| 70.3%
 
| 70.3%
 
| 81.3%
 
| 81.3%

Revision as of 13:48, 24 March 2009

  • Microsoft Research Paraphrase Corpus (MSRP)
  • see Dolan, Quirk, and Brockett (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 Type Accuracy F
MCS Mihalcea et al. (2006) unsupervised combination of several word similarity measures 70.3% 81.3%

References

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.

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.


See also