Concordance

Query I05-5003 19 >
Random sample 19 (885.8 per million)

measure(ment),3-1-I05-5003,bq	The task of <term> machine translation ( MT ) evaluation </term> is closely related to the task of <term> sentence-level semantic equivalence classification </term> .	#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	The task of <term> machine translation ( MT ) evaluation </term> is closely related to the task of <term> sentence-level semantic equivalence classification </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods ( BLEU , NIST , WER and PER ) </term> to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	This paper investigates the utility of applying standard <term> MT evaluation methods ( BLEU , NIST , WER and PER ) </term> to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods ( BLEU , NIST , WER and PER ) </term> to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </term> .	#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	This paper investigates the utility of applying standard <term> MT evaluation methods ( BLEU , NIST , WER and PER ) </term> to building <term> classifiers </term> to predict <term> semantic equivalence </term> and <term> entailment </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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	We also introduce a novel <term> classification method </term> based on <term> PER </term> which leverages <term> part of speech information </term> of the <term> words </term> contributing to the <term> word matches and non-matches </term> in the <term> sentence </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#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	Our <term> technique </term> gives a substantial improvement in <term> paraphrase classification accuracy </term> over all of the other <term> models </term> used in the experiments .	#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	Our <term> technique </term> gives a substantial improvement in <term> paraphrase classification accuracy </term> over all of the other <term> models </term> used in the experiments .	#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	Our <term> technique </term> gives a substantial improvement in <term> paraphrase classification accuracy </term> over all of the other <term> models </term> used in the experiments .	#8435 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the othermodels used in the experiments.


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