publication . Conference object . 2017

A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit

Amrith Krishna; Pavankumar Satuluri; Harshavardhan Ponnada; Muneeb Ahmed; Gulab Arora; Kaustubh Hiware; Pawan Goyal;
Open Access
  • Published: 27 Jul 2017
  • Publisher: Zenodo
Abstract
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.
Subjects
free text keywords: Sanskrit, Noun, Computer science, Artificial intelligence, business.industry, business, Existential quantification, Novelty, Identification (information), Semantic similarity, Productivity (linguistics), Natural language processing, computer.software_genre, computer, Sanskrit, language.human_language, language, Task (project management)
Communities
Communities with gateway
OpenAIRE Connect image
Download fromView all 2 versions
Open Access
ZENODO
Conference object . 2017
Providers: ZENODO
null
https://doi.org/10.18653/v1/w1...
Conference object . 2017
Providers: Crossref
Any information missing or wrong?Report an Issue