model,6-1-H94-1014,bq |
paper introduces a simple mixture
<term>
|
language model
|
</term>
that attempts to capture
<term>
long
|
#21217
This paper introduces a simple mixturelanguage model that attempts to capture long distance constraints in a sentence or paragraph. |
other,12-1-H94-1014,bq |
model
</term>
that attempts to capture
<term>
|
long distance constraints
|
</term>
in a
<term>
sentence
</term>
or
<term>
|
#21223
This paper introduces a simple mixture language model that attempts to capturelong distance constraints in a sentence or paragraph. |
other,17-1-H94-1014,bq |
long distance constraints
</term>
in a
<term>
|
sentence
|
</term>
or
<term>
paragraph
</term>
. The
<term>
|
#21228
This paper introduces a simple mixture language model that attempts to capture long distance constraints in asentence or paragraph. |
other,19-1-H94-1014,bq |
</term>
in a
<term>
sentence
</term>
or
<term>
|
paragraph
|
</term>
. The
<term>
model
</term>
is an
<term>
|
#21230
This paper introduces a simple mixture language model that attempts to capture long distance constraints in a sentence orparagraph. |
model,1-2-H94-1014,bq |
</term>
or
<term>
paragraph
</term>
. The
<term>
|
model
|
</term>
is an
<term>
m-component mixture
</term>
|
#21233
Themodel is an m-component mixture of trigram models. |
other,4-2-H94-1014,bq |
</term>
. The
<term>
model
</term>
is an
<term>
|
m-component mixture
|
</term>
of
<term>
trigram models
</term>
. The
|
#21236
The model is anm-component mixture of trigram models. |
model,7-2-H94-1014,bq |
<term>
m-component mixture
</term>
of
<term>
|
trigram models
|
</term>
. The models were constructed using
|
#21239
The model is an m-component mixture oftrigram models. |
lr,7-3-H94-1014,bq |
models were constructed using a 5K
<term>
|
vocabulary
|
</term>
and trained using a 76 million
<term>
|
#21249
The models were constructed using a 5Kvocabulary and trained using a 76 million word Wall Street Journal text corpus. |
other,14-3-H94-1014,bq |
</term>
and trained using a 76 million
<term>
|
word
|
</term><term>
Wall Street Journal text corpus
|
#21256
The models were constructed using a 5K vocabulary and trained using a 76 millionword Wall Street Journal text corpus. |
lr-prod,15-3-H94-1014,bq |
using a 76 million
<term>
word
</term><term>
|
Wall Street Journal text corpus
|
</term>
. Using the
<term>
BU recognition system
|
#21257
The models were constructed using a 5K vocabulary and trained using a 76 million wordWall Street Journal text corpus. |
lr,2-4-H94-1014,bq |
Journal text corpus
</term>
. Using the
<term>
|
BU recognition system
|
</term>
, experiments show a 7 % improvement
|
#21265
Using theBU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model. |
measure(ment),13-4-H94-1014,bq |
experiments show a 7 % improvement in
<term>
|
recognition accuracy
|
</term>
with the
<term>
mixture trigram models
|
#21276
Using the BU recognition system, experiments show a 7% improvement inrecognition accuracy with the mixture trigram models as compared to using a trigram model. |
model,17-4-H94-1014,bq |
recognition accuracy
</term>
with the
<term>
|
mixture trigram models
|
</term>
as compared to using a
<term>
trigram
|
#21280
Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with themixture trigram models as compared to using a trigram model. |
model,25-4-H94-1014,bq |
models
</term>
as compared to using a
<term>
|
trigram model
|
</term>
. This paper describes a method of
|
#21288
Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using atrigram model. |