measure(ment),7-2-H01-1070,bq |
The paper also proposes
<term>
rule-reduction algorithm
</term>
applying
<term>
mutual information
</term>
to reduce the
<term>
error-correction rules
</term>
.
|
#1269
The paper also proposes rule-reduction algorithm applyingmutual information to reduce the error-correction rules. |
measure(ment),7-3-H01-1070,bq |
Our
<term>
algorithm
</term>
reported more than 99 %
<term>
accuracy
</term>
in both
<term>
language identification
</term>
and
<term>
key prediction
</term>
.
|
#1284
Our algorithm reported more than 99%accuracy in both language identification and key prediction. |
model,10-1-H01-1070,bq |
This paper proposes a practical approach employing
<term>
n-gram models
</term>
and
<term>
error-correction rules
</term>
for
<term>
Thai key prediction
</term>
and
<term>
Thai-English language identification
</term>
.
|
#1251
This paper proposes a practical approach employing n-gram models anderror-correction rules for Thai key prediction and Thai-English language identification. |
model,12-2-H01-1070,bq |
The paper also proposes
<term>
rule-reduction algorithm
</term>
applying
<term>
mutual information
</term>
to reduce the
<term>
error-correction rules
</term>
.
|
#1274
The paper also proposes rule-reduction algorithm applying mutual information to reduce theerror-correction rules. |
model,7-1-H01-1070,bq |
This paper proposes a practical approach employing
<term>
n-gram models
</term>
and
<term>
error-correction rules
</term>
for
<term>
Thai key prediction
</term>
and
<term>
Thai-English language identification
</term>
.
|
#1248
This paper proposes a practical approach employingn-gram models and error-correction rules for Thai key prediction and Thai-English language identification. |
tech,1-3-H01-1070,bq |
Our
<term>
algorithm
</term>
reported more than 99 %
<term>
accuracy
</term>
in both
<term>
language identification
</term>
and
<term>
key prediction
</term>
.
|
#1278
Ouralgorithm reported more than 99% accuracy in both language identification and key prediction. |
tech,10-3-H01-1070,bq |
Our
<term>
algorithm
</term>
reported more than 99 %
<term>
accuracy
</term>
in both
<term>
language identification
</term>
and
<term>
key prediction
</term>
.
|
#1287
Our algorithm reported more than 99% accuracy in bothlanguage identification and key prediction. |
tech,13-1-H01-1070,bq |
This paper proposes a practical approach employing
<term>
n-gram models
</term>
and
<term>
error-correction rules
</term>
for
<term>
Thai key prediction
</term>
and
<term>
Thai-English language identification
</term>
.
|
#1254
This paper proposes a practical approach employing n-gram models and error-correction rules forThai key prediction and Thai-English language identification. |
tech,13-3-H01-1070,bq |
Our
<term>
algorithm
</term>
reported more than 99 %
<term>
accuracy
</term>
in both
<term>
language identification
</term>
and
<term>
key prediction
</term>
.
|
#1290
Our algorithm reported more than 99% accuracy in both language identification andkey prediction. |
tech,17-1-H01-1070,bq |
This paper proposes a practical approach employing
<term>
n-gram models
</term>
and
<term>
error-correction rules
</term>
for
<term>
Thai key prediction
</term>
and
<term>
Thai-English language identification
</term>
.
|
#1258
This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction andThai-English language identification. |
tech,4-2-H01-1070,bq |
The paper also proposes
<term>
rule-reduction algorithm
</term>
applying
<term>
mutual information
</term>
to reduce the
<term>
error-correction rules
</term>
.
|
#1266
The paper also proposesrule-reduction algorithm applying mutual information to reduce the error-correction rules. |