| tech,22-2-I05-5003,bq | , WER and PER ) </term> to building <term> | classifiers | </term> to predict <term> semantic equivalence | #8359 This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence and entailment. |
| other,18-3-I05-5003,bq | of speech information </term> of the <term> | words | </term> contributing to the <term> word matches | #8385 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
| other,12-4-I05-5003,bq | </term> are able to produce useful <term> | features | </term> for <term> paraphrase classification | #8409 Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. |
| tech,15-5-I05-5003,bq | accuracy </term> over all of the other <term> | models | </term> used in the experiments . We propose | #8435 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. |
