other,40-1-N01-1003,bq |
how to combine them into one or more
<term>
|
sentences
|
</term>
. In this paper , we present
<term>
|
#1333
Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or moresentences. |
|
<term>
English stemmer
</term>
and a small ( 10K
|
sentences
|
)
<term>
parallel corpus
</term>
as its sole
|
#4466
The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
other,9-2-C04-1106,bq |
quite dubious about
<term>
analogies between
|
sentences
|
</term>
: they would not be enough numerous
|
#5895
But computational linguists seem to be quite dubious about analogies between sentences: they would not be enough numerous to be of any use. |
other,16-3-C04-1106,bq |
of
<term>
analogies
</term>
among the
<term>
|
sentences
|
</term>
that it contains . We give two estimates
|
#5925
We report experiments conducted on a multilingual corpus to estimate the number of analogies among thesentences that it contains. |
other,10-1-N04-1024,bq |
</term>
includes a capability that labels
<term>
|
sentences
|
</term>
in student
<term>
writing
</term>
with
|
#6655
CriterionSM Online Essay Evaluation Service includes a capability that labelssentences in student writing with essay-based discourse elements (e.g., thesis statements). |
other,5-3-N04-1024,bq |
identifies
<term>
features
</term>
of
<term>
|
sentences
|
</term>
based on
<term>
semantic similarity
|
#6695
This system identifies features ofsentences based on semantic similarity measures and discourse structure. |
other,34-3-I05-2021,bq |
<term>
words
</term>
in
<term>
source language
|
sentences
|
</term>
. Surprisingly however , the
<term>
|
#7892
At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences. |
other,10-1-I05-5008,bq |
<term>
paraphrase
</term>
sets from
<term>
seed
|
sentences
|
</term>
to be used as
<term>
reference sets
|
#8452
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. |
other,25-2-I05-5008,bq |
grammaticality
</term>
: at least 99 % correct
<term>
|
sentences
|
</term>
; ( ii ) their
<term>
equivalence in
|
#8495
We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correctsentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
tech,6-1-J05-4003,bq |
method
</term>
for
<term>
discovering parallel
|
sentences
|
</term>
in
<term>
comparable , non-parallel
|
#8991
We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. |
other,12-2-J05-4003,bq |
classifier
</term>
that , given a pair of
<term>
|
sentences
|
</term>
, can reliably determine whether
|
#9010
We train a maximum entropy classifier that, given a pair ofsentences, can reliably determine whether or not they are translations of each other. |
other,13-2-E06-1031,bq |
result in correct or almost correct
<term>
|
sentences
|
</term>
. In this paper , we will present
|
#10352
In many cases though such movements still result in correct or almost correctsentences. |
other,9-4-P06-2059,bq |
we can automatically extract such
<term>
|
sentences
|
</term>
that express opinion . In our experiment
|
#11450
By using them, we can automatically extract suchsentences that express opinion. |
other,13-5-P06-2059,bq |
corpus
</term>
consisting of 126,610
<term>
|
sentences
|
</term>
. This paper examines what kind of
|
#11468
In our experiment, the method could construct a corpus consisting of 126,610sentences. |
other,4-2-P06-4011,bq |
articles
</term>
. In our approach ,
<term>
|
sentences
|
</term>
in a given
<term>
abstract
</term>
are
|
#11717
In our approach,sentences in a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions. |
other,8-2-A88-1001,bq |
and heuristically-produced complete
<term>
|
sentences
|
</term>
in
<term>
text
</term>
or
<term>
text-to-speech
|
#14890
Multimedia answers include videodisc images and heuristically-produced completesentences in text or text-to-speech form. |
other,33-4-C88-2086,bq |
presuppositional nature
</term>
of these
<term>
|
sentences
|
</term>
. We have developed a
<term>
computational
|
#15432
By reappraising these insightful counterexamples, the inferential theory for natural language presuppositions described in /Mercer 1987, 1988/ gives a simple and straightforward explanation for the presuppositional nature of thesesentences. |
other,5-5-C88-2160,bq |
<term>
paraphrasing
</term>
ambiguous
<term>
|
sentences
|
</term>
are presented . Computer programs
|
#15733
Some examples of paraphrasing ambiguoussentences are presented. |
other,18-1-C90-2032,bq |
<term>
dependency structure
</term>
of
<term>
|
sentences
|
</term>
. The
<term>
DoPS system
</term>
extracts
|
#16302
This paper proposes document oriented preference sets(DoPS) for the disambiguation of the dependency structure ofsentences. |
other,14-4-C90-2032,bq |
structures
</term>
of
<term>
Japanese patent claim
|
sentences
|
</term>
. This paper describes the framework
|
#16353
Implementation and empirical results are described for the the analysis of dependency structures of Japanese patent claim sentences. |