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. |
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,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. |
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. |
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. |
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. |
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. |
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. |
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,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,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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |