In this paper, we build morphological chains for agglutinative languages by using a log-linear model for the morphological segmentation task. The model is based on the unsupervised morphological segmentation system called MorphoChains. We extend MorphoChains log linear model by expanding the candidate space recursively to cover more split points for agglutinative languages such as Turkish, whereas in the original model candidates are generated by considering only binary segmentation of each word. The results show that we improve the state-of-art Turkish scores by 12 having a F-measure of 72 and we improve the English scores by 3 having a F-measure of 74 . Eventually, the system outperforms both MorphoChains and other well-known unsupervised morphological segmentation systems. The results indicate that candidate generation plays an important role in such an unsupervised log-linear model that is learned using contrastive estimation with negative samples.
Probabilistic methods have also been used in unsupervised morphological segmentation. Creutz and Lagus @cite_14 introduce another well-known unsupervised morphological segmentation Morfessor Baseline, the first member of the Morfessor family. One of the versions is based on MDL principle and the other one is based on Maximum Likelihood (ML) estimate. In another member of the same family, Creutz and Lagus @cite_10 , suggest using priors by converting the model into a Maximum a Posteriori model, thereby introducing another member of the same family, called Morfessor Categories MAP (Maximum A-posterior). Morfessor has been one of the main reference segmentation systems to compare with most of the unsupervised segmentation systems. In this paper, we also compare our extended model with Morfessor Baseline and Morfessor CatMAP.
@cite_14 @cite_10
2117621558 201532657
We present two methods for unsupervised segmentation of words into morpheme-like units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current state-of-the-art system. This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the “meaning” and “form” of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.