Towards a Malay derivational lexicon: learning affixes using expectation maximization
We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a na¨ıve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the...
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| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | en |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/32082/1/W11-3005.pdf http://irep.iium.edu.my/32082/ http://aclweb.org/anthology//W/W11/W11-3005.pdf |
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| Summary: | We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of
morphological segmentations produced by a na¨ıve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given
segmentation. We train the model with EM to find the segmentation that maximizes the probability of each morpheme. We extract the set of affix patterns produced
by our algorithm and evaluate them against two references: a list of affix patterns extracted from our hand-segmented
derivational wordlist and a derivational history produced by a stemmer. |
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