| tech,3-1-I05-5003,ak | volume from the Web . The task of <term> | machine translation ( MT ) evaluation | </term> is closely related to the task of | #7369 The task ofmachine translation ( MT ) evaluation is closely related to the task of sentence-level semantic equivalence classification. | |
| tech,16-1-I05-5003,ak | </term> is closely related to the task of <term> | sentence-level semantic equivalence classification | </term> . This paper investigates the utility | #7382 The task of machine translation (MT) evaluation is closely related to the task ofsentence-level semantic equivalence classification. | |
| tech,8-2-I05-5003,ak | investigates the utility of applying standard <term> | MT evaluation methods | </term> ( <term> BLEU </term> , <term> NIST </term> | #7395 This paper investigates the utility of applying standardMT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence and entailment. | |
| measure(ment),12-2-I05-5003,ak | <term> MT evaluation methods </term> ( <term> | BLEU | </term> , <term> NIST </term> , <term> WER </term> | #7399 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. | |
| measure(ment),14-2-I05-5003,ak | methods </term> ( <term> BLEU </term> , <term> | NIST | </term> , <term> WER </term> and <term> PER </term> | #7401 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. | |
| measure(ment),16-2-I05-5003,ak | <term> BLEU </term> , <term> NIST </term> , <term> | WER | </term> and <term> PER </term> ) to building <term> | #7403 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. | |
| measure(ment),18-2-I05-5003,ak | <term> NIST </term> , <term> WER </term> and <term> | PER | </term> ) to building <term> classifiers </term> | #7405 This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER andPER) to building classifiers to predict semantic equivalence and entailment. | |
| tech,22-2-I05-5003,ak | </term> and <term> PER </term> ) to building <term> | classifiers | </term> to predict <term> semantic equivalence | #7409 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,ak | <term> classifiers </term> to predict <term> | semantic equivalence | </term> and <term> entailment </term> . We also | #7412 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,ak | <term> semantic equivalence </term> and <term> | entailment | </term> . We also introduce a novel <term> | #7415 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,ak | </term> . We also introduce a novel <term> | classification method | </term> based on <term> PER </term> which leverages | #7422 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,ak | classification method </term> based on <term> | PER | </term> which leverages <term> part of speech | #7426 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,ak | on <term> PER </term> which leverages <term> | part of speech information | </term> of the words contributing to the <term> | #7429 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,22-3-I05-5003,ak | </term> of the words contributing to the <term> | word matches | </term> and non-matches in the <term> sentence | #7439 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,ak | matches </term> and non-matches in the <term> | sentence | </term> . Our results show that <term> MT evaluation | #7445 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. | |
| tech,4-4-I05-5003,ak | sentence </term> . Our results show that <term> | MT evaluation techniques | </term> are able to produce useful features | #7451 Our results show thatMT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. | |
| tech,14-4-I05-5003,ak | able to produce useful features for <term> | paraphrase classification | </term> and to a lesser extent <term> entailment | #7461 Our results show that MT evaluation techniques are able to produce useful features forparaphrase classification and to a lesser extent entailment. | |
| other,21-4-I05-5003,ak | classification </term> and to a lesser extent <term> | entailment | </term> . Our technique gives a substantial | #7468 Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extententailment. | |
| tech,7-5-I05-5003,ak | gives a substantial improvement in <term> | paraphrase classification accuracy | </term> over all of the other models used | #7477 Our technique gives a substantial improvement inparaphrase classification accuracy over all of the other models used in the experiments. |
