tailieunhanh - Báo cáo khoa học: "Probabilistic Hierarchical Clustering of Morphological Paradigms"

We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-ofart systems. | Probabilistic Hierarchical Clustering of Morphological Paradigms Burcu Can Department of Computer Science University of York Heslington York YO10 5GH UK burcucan@ Suresh Manandhar Department of Computer Science University of York Heslington York YO10 5GH UK suresh@ Abstract We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using Kurimo et al. 2011a Kurimo et al. 2011b dataset shows that our method performs competitively when compared with current state-of-art systems. 1 Introduction Unsupervised morphological segmentation of a text involves learning rules for segmenting words into their morphemes. Morphemes are the smallest meaning bearing units of words. The learning process is fully unsupervised using only raw text as input to the learning system. For example the word respectively is split into morphemes respect ive and ly. Many fields such as machine translation information retrieval speech recognition etc. require morphological segmentation since new words are always created and storing all the word forms will require a massive dictionary. The task is even more complex when morphologically complicated languages . agglutinative languages are considered. The sparsity problem is more severe for more morphologically complex languages. Applying morphological segmentation mitigates data sparsity by tackling the issue with out-of-vocabulary OOV words. In this paper we propose a paradigmatic approach. A morphological paradigm is a pair StemList SuffixList such that each concatenation of Stem Suffix where Stem G StemList and Suffix G SuffixList is a valid word form. The learning of morphological paradigms is not novel as there has already been existing work .

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