Summary of the paper
| Title | Predicting Morphological Types of Chinese Bi-Character Words by Machine Learning Approaches |
|---|---|
| Authors | Ting-Hao Huang, Lun-Wei Ku and Hsin-Hsi Chen |
| Abstract | 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. |
| Topics | Morphology, Corpus (creation, annotation, etc.), Lexicon, lexical database |
Full paper |
Predicting Morphological Types of Chinese Bi-Character Words by Machine Learning Approaches |
Slides |
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| Bibtex | @InProceedings{HUANG10.397, author = {Ting-Hao Huang and Lun-Wei Ku and Hsin-Hsi Chen}, title = {Predicting Morphological Types of Chinese Bi-Character Words by Machine Learning Approaches}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |