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.
Some of the systems not only attempt to perform morphological segmentation, but also aim to learn hidden structures behind words. Chan @cite_4 applies Latent Dirichlet Allocation (LDA) to learn morphological paradigms as latent classes. The model assumes that correct segmentations of words are known but morphological paradigms are to be learned. Chan discovers that the final morphological paradigms can be matched with syntactic tags (such as noun, verb etc.). Can and Manandhar @cite_11 obtain syntactic categories from a context distributional clustering algorithm @cite_1 and learn paradigms by using the the pairs of syntactic categories that have common stems.
@cite_1 @cite_4 @cite_11
2139090905 2063131977
This paper addresses the issue of the automatic induction of syntactic categories from unannotated corpora. Previous techniques give good results, but fail to cope well with ambiguity or rare words. An algorithm, context distribution clustering (CDC), is presented which can be naturally extended to handle these problems. This paper introduces the probabilistic paradigm, a probabilistic, declarative model of morphological structure. We describe an algorithm that recursively applies Latent Dirichlet Allocation with an orthogonality constraint to discover morphological paradigms as the latent classes within a suffix-stem matrix. We apply the algorithm to data preprocessed in several different ways, and show that when suffixes are distinguished for part of speech and allomorphs or gender conjugational variants are merged, the model is able to correctly learn morphological paradigms for English and Spanish. We compare our system with Linguistica (Goldsmith 2001), and discuss the advantages of the probabilistic paradigm over Linguistica's signature representation.