@InProceedings{krishna-EtAl:2017:TextGraphs-11,
  author    = {Krishna, Amrith  and  Satuluri, Pavankumar  and  Ponnada, Harshavardhan  and  Ahmed, Muneeb  and  Arora, Gulab  and  Hiware, Kaustubh  and  Goyal, Pawan},
  title     = {A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit},
  booktitle = {Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {66--75},
  abstract  = {Derivational nouns are widely used in Sanskrit corpora and represent an
	important cornerstone of productivity in the language. Currently there exists
	no analyser that identifies the derivational nouns. We propose a semi
	supervised approach for identification of derivational nouns in Sanskrit. We
	not only identify the derivational words, but also link them to their
	corresponding source words. Our novelty comes in the design of the network
	structure for the task. The edge weights are featurised based on the phonetic,
	morphological, syntactic and the semantic similarity shared between the words
	to be identified. We find that our model is effective for the task, even when
	we employ a labelled dataset which is only 5 \% to that of the entire dataset.},
  url       = {http://www.aclweb.org/anthology/W17-2409}
}