TY - GEN
T1 - Predicting morphological types of Chinese bi-character words by machine learning approaches
AU - Huang, Ting Hao
AU - Ku, Lun Wei
AU - Chen, Hsin Hsi
PY - 2010
Y1 - 2010
N2 - This paper presented an overview of Chinese bi-character words' morphological types, and proposed a set of features for machine learning approaches to predict these types based on composite characters' information. First, eight morphological types were defined, and 6,500 Chinese bi-character words were annotated with these types. After pre-processing, 6,178 words were selected to construct a corpus named Reduced Set. We analyzed Reduced Set and conducted the inter-annotator agreement test. The average kappa value of 0.67 indicates a substantial agreement. Second, Bi-character words' morphological types are considered strongly related with the composite characters' parts of speech in this paper, so we proposed a set of features which can simply be extracted from dictionaries to indicate the characters' "tendency" of parts of speech. Finally, we used these features and adopted three machine learning algorithms, SVM, CRF, and Naïve Bayes, to predict the morphological types. On the average, the best algorithm CRF achieved 75% of the annotators' performance.
AB - This paper presented an overview of Chinese bi-character words' morphological types, and proposed a set of features for machine learning approaches to predict these types based on composite characters' information. First, eight morphological types were defined, and 6,500 Chinese bi-character words were annotated with these types. After pre-processing, 6,178 words were selected to construct a corpus named Reduced Set. We analyzed Reduced Set and conducted the inter-annotator agreement test. The average kappa value of 0.67 indicates a substantial agreement. Second, Bi-character words' morphological types are considered strongly related with the composite characters' parts of speech in this paper, so we proposed a set of features which can simply be extracted from dictionaries to indicate the characters' "tendency" of parts of speech. Finally, we used these features and adopted three machine learning algorithms, SVM, CRF, and Naïve Bayes, to predict the morphological types. On the average, the best algorithm CRF achieved 75% of the annotators' performance.
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M3 - Conference contribution
AN - SCOPUS:85037079802
T3 - Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010
SP - 844
EP - 850
BT - Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010
A2 - Tapias, Daniel
A2 - Russo, Irene
A2 - Hamon, Olivier
A2 - Piperidis, Stelios
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Maegaard, Bente
A2 - Odijk, Jan
A2 - Rosner, Mike
PB - European Language Resources Association (ELRA)
T2 - 7th International Conference on Language Resources and Evaluation, LREC 2010
Y2 - 17 May 2010 through 23 May 2010
ER -