Work

A Graph Based Semi-Supervised Approach For Analysis Of Derivational Nouns In Sanskrit

Year: 2017

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.583788

Authors: Krishna, Amrith, Satuluri, Pavankumar, Ahmed, Muneeb, Goyal, Pawan, Ponnada, Harshavardhan, Arora, Gulab, Hiware, Kaustubh

Institutions: Indian Institute of Technology Kharagpur, Indian Institute of Technology BHU

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4300553970

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