|
<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. |
lr,3-3-H92-1026,bq |
way . We use a
<term>
corpus of bracketed
|
sentences
|
</term>
, called a
<term>
Treebank
</term>
,
|
#18949
We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. |
lr-prod,12-6-H92-1060,bq |
system
</term>
on the
<term>
February '92 test
|
sentences
|
</term>
, and discuss some issues with regard
|
#19513
We also report here on the performance of the system on the February '92 test sentences, and discuss some issues with regard to the evaluation methodology. |
other,0-2-A94-1007,bq |
Syntactic analysis of the English coordinate
|
sentences
|
</term>
is one of the most difficult problems
|
#19705
Syntactic analysis of the English coordinate sentences is one of the most difficult problems for machine translation (MT) systems. |
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,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,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,12-5-C90-3063,bq |
<term>
pronoun
</term><term>
it
</term>
in
<term>
|
sentences
|
</term>
that were randomly selected from
|
#16682
An experiment was performed to resolve references of the pronoun it insentences that were randomly selected from the corpus. |
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,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,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. |
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,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,24-4-H92-1060,bq |
of robustly parsed vs. fully parsed
<term>
|
sentences
|
</term>
on the
<term>
October '91 dry-run test
|
#19459
We have assessed the degree of success of the robust parsing mechanism through a breakdown of the performance of robustly parsed vs. fully parsedsentences on the October '91 dry-run test set. |
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. |
other,31-3-N04-1022,bq |
</term>
of
<term>
source and target language
|
sentences
|
</term>
. We report the performance of the
|
#6610
We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. |
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,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,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,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. |