Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
This addresses the challenge of processing Sanskrit, where traditional methods are limited due to free word order, offering a significant performance boost for linguistic and computational tasks in this domain.
The paper tackles the problem of word segmentation and morphological tagging in Sanskrit, a free word order language, by proposing a structured prediction framework using an energy-based model. It achieves a state-of-the-art F-Score of 96.92, a 7.06% improvement, while using less than one-tenth of the task-specific training data.
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.