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The configurational information in sentences of a free word order languagesuch as Sanskrit is of limited use. Thus, the context of the entire sentencewill be desirable even for basic processing tasks such as word segmentation. Wepropose a structured prediction framework that jointly solves the wordsegmentation and morphological tagging tasks in Sanskrit. We build an energybased model where we adopt approaches generally employed in graph based parsingtechniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms thestate of the art with an F-Score of 96.92 (percentage improvement of 7.06%)while using less than one-tenth of the task-specific training data. We findthat the use of a graph based ap- proach instead of a traditional lattice-basedsequential labelling approach leads to a percentage gain of 12.6% in F-Scorefor the segmentation task.

Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit