Conference paper Open Access
Krishna, Amrith; Satuluri, Pavankumar; Ahmed, Muneeb; Goyal, Pawan; Ponnada, Harshavardhan; Arora, Gulab; Hiware, Kaustubh
The work is accepted at TextGraphs - 17 colocated with ACL 2017 (http://acl2017.org/)
Derivational nouns are widely used in Sanskrit corpora and is a prevalent means 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. The novelty of our work is primarily in its 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.