TY - GEN
T1 - Markov Models for Written Language Identification and Verification
AU - Tran, Dat
AU - Sharma, Dharmendra
PY - 2005
Y1 - 2005
N2 - —The paper presents a Markov chain-based method for automatic written language identification. Given a training document in a specific language, each word can be represented as a Markov chain of letters. Using the entire training document regarded as a set of Markov chains, the set of initial and transition probabilities can be calculated and referred to as a Markov model for that language. Given an unknown language string, the maximum likelihood decision rule was used to identify language. Experimental results showed that the proposed method achieved lower error rate and faster identification speed than the current n-gram method.
AB - —The paper presents a Markov chain-based method for automatic written language identification. Given a training document in a specific language, each word can be represented as a Markov chain of letters. Using the entire training document regarded as a set of Markov chains, the set of initial and transition probabilities can be calculated and referred to as a Markov model for that language. Given an unknown language string, the maximum likelihood decision rule was used to identify language. Experimental results showed that the proposed method achieved lower error rate and faster identification speed than the current n-gram method.
UR - http://ise.canberra.edu.au/html/DTran/Publications/P51479.pdf
UR - https://www.mendeley.com/catalogue/30029f2e-c643-3bd1-b890-7b27eb49f8f5/
M3 - Conference contribution
T3 - Proceedings of the 12th international conference on neural information processing
SP - 67
EP - 70
BT - Proceedings of the 12th International Conference on Neural Information Processing
A2 - Yang, K.Y
A2 - Dung, L.R
PB - National Chiao Tung University
CY - Taiwan
T2 - International Conference on Neural Information Processing
Y2 - 29 October 2005 through 2 November 2005
ER -