  The configurational information in sentences of a free word order language
such as Sanskrit is of limited use. Thus, the context of the entire sentence
will be desirable even for basic processing tasks such as word segmentation. We
propose a structured prediction framework that jointly solves the word
segmentation and morphological tagging tasks in Sanskrit. We build an energy
based model where we adopt approaches generally employed in graph based parsing
techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the
state 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 find
that the use of a graph based ap- proach instead of a traditional lattice-based
sequential labelling approach leads to a percentage gain of 12.6% in F-Score
for the segmentation task.
