Jivnesh Sandhan | DeepAI

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∙ 08/17/2023

Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit

The primary focus of this thesis is to make Sanskrit manuscripts more ac...

0 Jivnesh Sandhan, et al. ∙

∙ 08/14/2023

Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastaka

Sanskrit poetry has played a significant role in shaping the literary an...

0 Jivnesh Sandhan, et al. ∙

∙ 02/19/2023

SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes

We present a neural Sanskrit Natural Language Processing (NLP) toolkit n...

0 Jivnesh Sandhan, et al. ∙

∙ 10/21/2022

TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer

Sanskrit Word Segmentation (SWS) is essential in making digitized texts ...

0 Jivnesh Sandhan, et al. ∙

∙ 08/22/2022

A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit

The phenomenon of compounding is ubiquitous in Sanskrit. It serves for a...

0 Jivnesh Sandhan, et al. ∙

∙ 01/27/2022

Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages

Nowadays, code-mixing has become ubiquitous in Natural Language Processi...

0 Jivnesh Sandhan, et al. ∙

∙ 01/27/2022

Systematic Investigation of Strategies Tailored for Low-Resource Settings for Sanskrit Dependency Parsing

Existing state of the art approaches for Sanskrit Dependency Parsing (SD...

0 Jivnesh Sandhan, et al. ∙

∙ 04/01/2021

Evaluating Neural Word Embeddings for Sanskrit

Recently, the supervised learning paradigm's surprisingly remarkable per...

0 Jivnesh Sandhan, et al. ∙

∙ 02/12/2021

A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages

Neural dependency parsing has achieved remarkable performance for many d...

20 Jivnesh Sandhan, et al. ∙

∙ 04/17/2020

Neural Approaches for Data Driven Dependency Parsing in Sanskrit

Data-driven approaches for dependency parsing have been of great interes...

0 Amrith Krishna, et al. ∙

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