Concordance

Query I05-5003 19 (608.3 per million)

tech,3-1-I05-5003,ak	The task of <term> machine translation ( MT ) evaluation </term> is closely related to the task of <term> sentence-level semantic equivalence classification </term> .	#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	The task of <term> machine translation ( MT ) evaluation </term> is closely related to the task of <term> sentence-level semantic equivalence classification </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods </term> ( <term> BLEU </term> , <term> NIST </term> , <term> WER </term> and <term> PER </term> ) to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the words contributing to the <term> word matches </term> and non-matches in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the words contributing to the <term> word matches </term> and non-matches in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the words contributing to the <term> word matches </term> and non-matches in the <term> sentence </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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the words contributing to the <term> word matches </term> and non-matches in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the words contributing to the <term> word matches </term> and non-matches in the <term> sentence </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful features for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful features for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful features for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our technique gives a substantial improvement in <term> paraphrase classification accuracy </term> over all of the other models used in the experiments .	#7477 Our technique gives a substantial improvement inparaphrase classification accuracy over all of the other models used in the experiments.


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