| measure(ment),3-1-I05-5003,bq | from the <term> Web </term> . The task of <term> | machine translation ( MT ) evaluation | </term> is closely related to the task of | #8319 The task ofmachine translation ( MT ) evaluation is closely related to the task of sentence-level semantic equivalence classification. |
| tech,16-1-I05-5003,bq | </term> is closely related to the task of <term> | sentence-level semantic equivalence classification | </term> . This paper investigates the utility | #8332 The task of machine translation (MT) evaluation is closely related to the task ofsentence-level semantic equivalence classification. |
| measure(ment),8-2-I05-5003,bq | investigates the utility of applying standard <term> | MT evaluation methods ( BLEU , NIST , WER and PER ) | </term> to building <term> classifiers </term> | #8345 This paper investigates the utility of applying standardMT evaluation methods ( BLEU , NIST , WER and PER ) to building classifiers to predict semantic equivalence and entailment. |
| 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 buildingclassifiers to predict semantic equivalence and entailment. |
| other,25-2-I05-5003,bq | <term> classifiers </term> to predict <term> | semantic equivalence | </term> and <term> entailment </term> . We also | #8362 This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predictsemantic equivalence and entailment. |
| other,28-2-I05-5003,bq | <term> semantic equivalence </term> and <term> | entailment | </term> . We also introduce a novel <term> | #8365 This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence andentailment. |
| tech,5-3-I05-5003,bq | </term> . We also introduce a novel <term> | classification method | </term> based on <term> PER </term> which leverages | #8372 We also introduce a novelclassification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
| measure(ment),9-3-I05-5003,bq | classification method </term> based on <term> | PER | </term> which leverages <term> part of speech | #8376 We also introduce a novel classification method based onPER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
| other,12-3-I05-5003,bq | on <term> PER </term> which leverages <term> | part of speech information | </term> of the <term> words </term> contributing | #8379 We also introduce a novel classification method based on PER which leveragespart of speech information of the words contributing to the word matches and non-matches in the sentence. |
| 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 thewords contributing to the word matches and non-matches in the sentence. |
| other,22-3-I05-5003,bq | <term> words </term> contributing to the <term> | word matches and non-matches | </term> in the <term> sentence </term> . Our | #8389 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to theword matches and non-matches in the sentence. |
| other,28-3-I05-5003,bq | matches and non-matches </term> in the <term> | sentence | </term> . Our results show that <term> MT evaluation | #8395 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 thesentence. |
| measure(ment),4-4-I05-5003,bq | sentence </term> . Our results show that <term> | MT evaluation techniques | </term> are able to produce useful <term> features | #8401 Our results show thatMT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. |
| other,12-4-I05-5003,bq | techniques </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 usefulfeatures for paraphrase classification and to a lesser extent entailment. |
| tech,14-4-I05-5003,bq | produce useful <term> features </term> for <term> | paraphrase classification | </term> and to a lesser extent <term> entailment | #8411 Our results show that MT evaluation techniques are able to produce useful features forparaphrase classification and to a lesser extent entailment. |
| tech,21-4-I05-5003,bq | classification </term> and to a lesser extent <term> | entailment | </term> . Our <term> technique </term> gives | #8418 Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extententailment. |
| tech,1-5-I05-5003,bq | extent <term> entailment </term> . Our <term> | technique | </term> gives a substantial improvement in | #8421 Ourtechnique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. |
| measure(ment),7-5-I05-5003,bq | gives a substantial improvement in <term> | paraphrase classification accuracy | </term> over all of the other <term> models | #8427 Our technique gives a substantial improvement inparaphrase classification accuracy over all of the other models used in the experiments. |
| 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 othermodels used in the experiments. |
