Sasi Prasanth Bandaru


Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Amrith Krishna | Bishal Santra | Sasi Prasanth Bandaru | Gaurav Sahu | Vishnu Dutt Sharma | Pavankumar Satuluri | Pawan Goyal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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 approach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6% in F-Score for the segmentation task.


Word Segmentation in Sanskrit Using Path Constrained Random Walks
Amrith Krishna | Bishal Santra | Pavankumar Satuluri | Sasi Prasanth Bandaru | Bhumi Faldu | Yajuvendra Singh | Pawan Goyal
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.