|
will demonstrate an application of this
|
approach
|
called
<term>
LCS-Marine
</term>
. Using
<term>
|
#824
We have built and will demonstrate an application of this approach called LCS-Marine. |
|
confidence
</term>
. We describe a three-tiered
|
approach
|
for
<term>
evaluation
</term>
of
<term>
spoken
|
#1199
We describe a three-tiered approach for evaluation of spoken dialogue systems. |
|
performance
</term>
. We describe our use of this
|
approach
|
in numerous fielded
<term>
user studies
</term>
|
#1229
We describe our use of this approach in numerous fielded user studies conducted with the U.S. military. |
|
military . This paper proposes a practical
|
approach
|
employing
<term>
n-gram models
</term>
and
<term>
|
#1246
This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. |
|
of the same
<term>
source text
</term>
. Our
|
approach
|
yields
<term>
phrasal and single word lexical
|
#1801
Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. |
|
operational semantics
</term>
. The value of this
|
approach
|
is that as the
<term>
operational semantics
|
#1907
The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. |
tech,21-1-N03-1004,bq |
developed a
<term>
multi-strategy and multi-source
|
approach
|
to question answering
</term>
which is based
|
#2330
Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. |
tech,2-1-N03-2025,bq |
quality
</term>
. A novel
<term>
bootstrapping
|
approach
|
</term>
to
<term>
Named Entity ( NE ) tagging
|
#3288
A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. |
|
successive learners
</term>
is presented . This
|
approach
|
only requires a few
<term>
common noun
</term>
|
#3306
This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
tech,0-4-N03-3010,bq |
robustness and flexibility .
<term>
Statistical
|
approach
|
</term>
is much more robust but less accurate
|
#3535
Statistical approach is much more robust but less accurate. |
|
<term>
corpus data
</term>
. We describe a new
|
approach
|
which involves clustering
<term>
subcategorization
|
#3906
We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. |
tech,3-1-P03-1022,bq |
</term>
. We apply a
<term>
decision tree based
|
approach
|
</term>
to
<term>
pronoun resolution
</term>
|
#3979
We apply a decision tree based approach to pronoun resolution in spoken dialogue. |
tech,4-1-P03-1050,bq |
paper presents an
<term>
unsupervised learning
|
approach
|
</term>
to building a
<term>
non-English (
|
#4436
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. |
|
be given for
<term>
Arabic
</term>
, but the
|
approach
|
is applicable to any
<term>
language
</term>
|
#4521
Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. |
tech,1-6-P03-1050,bq |
removal
</term>
. Our
<term>
resource-frugal
|
approach
|
</term>
results in 87.5 %
<term>
agreement
</term>
|
#4534
Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. |
|
learning
</term>
. In this paper , we evaluate an
|
approach
|
to automatically acquire
<term>
sense-tagged
|
#4828
In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. |
|
presents a
<term>
machine learning
</term>
|
approach
|
to bare
<term>
sluice disambiguation
</term>
|
#5155
This paper presents a machine learningapproach to bare sluice disambiguation in dialogue. |
|
between
<term>
objects
</term>
. However , such an
|
approach
|
does not work well when there is no distinctive
|
#5635
However, such an approach does not work well when there is no distinctive attribute among objects. |
tech,12-2-C04-1112,bq |
</term>
, we introduce a
<term>
lemma-based
|
approach
|
</term>
. The advantage of this novel method
|
#6022
Instead of building individual classifiers per ambiguous wordform, we introduce a lemma-based approach. |
|
text as a coherent
<term>
corpus
</term>
. Our
|
approach
|
is based on the idea that one person tends
|
#6140
Our approach is based on the idea that one person tends to use one expression for one meaning. |