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


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