Probabilistic Hierarchical Clustering of Morphological Paradigms

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 654–663, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Probabilistic Hierarchical Clustering of Morphological Paradigms Burcu Can Department of Computer Science University of York Heslington, York, YO10 5GH, UK burcucan@gmail.com Suresh Manandhar Department of Computer Science University of York Heslington, York, YO10 5GH, UK suresh@cs.york.ac.uk Abstract We propose a novel method for learning morphological paradigms that are struc- tured within a hierarchy. The hierarchi- cal structuring of paradigms groups mor- phologically similar words close to each other in a tree structure. This allows detect- ing morphological similarities easily lead- ing to improved morphological segmen- tation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competi- tively 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 small- est meaning bearing units of words. The learn- ing process is fully unsupervised, using only raw text as input to the learning system. For example, the word respectively is split into morphemes re- spect, ive and ly. Many fields, such as machine translation, information retrieval, speech recog- nition etc., require morphological segmentation since new words are always created and storing all the word forms will require a massive dictio- nary. The task is even more complex, when mor- phologically complicated languages (i.e. agglu- tinative languages) are considered. The sparsity problem is more severe for more morphologically complex languages. Applying morphological seg- mentation mitigates data sparsity by tackling the issue with out-of-vocabulary (OOV) words. In this paper, we propose a paradigmatic ap- proach. A morphological paradigm is a pair (StemList, SuffixList) such that each concatena- tion of Stem+Suffix (where Stem ∈ StemList and Suffix ∈ SuffixList) is a valid word form. The learning of morphological paradigms is not novel as there has already been existing work in this area such as Goldsmith (2001), Snover et al. (2002), Monson et al. (2009), Can and Manandhar (2009) and Dreyer and Eisner (2011). However, none of these existing approaches address learning of the hierarchical structure of paradigms. Hierarchical organisation of words help cap- ture morphological similarities between words in a compact structure by factoring these similarities through stems, suffixes or prefixes. Our inference algorithm simultaneously infers latent variables (i.e. the morphemes) along with their hierarchical organisation. Most hierarchical clustering algo- rithms are single-pass, where once the hierarchi- cal structure is built, the structure does not change further. The paper is structured as follows: section 2 gives the related work, section 3 describes the probabilistic hierarchical clustering scheme, sec- tion 4 explains the morphological segmenta- tion model by embedding it into the clustering scheme and describes the inference algorithm along with how the morphological segmentation is performed, section 5 presents the experiment settings along with the evaluation scores, and fi- nally section 6 presents a discussion with a com- parison with other systems …

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