Search Results for author:
Found 11 papers, 9 papers with code
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit
This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them.
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis.
Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastaka
We provide a deep analysis of Siksastaka, a Sanskrit poem, from the perspective of 6 prominent kavyashastra schools, to illustrate the proposed framework.
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 named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification.
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer
Existing lexicon driven approaches for SWS make use of Sanskrit Heritage Reader, a lexicon-driven shallow parser, to generate the complete candidate solution space, over which various methods are applied to produce the most valid solution.
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit
The phenomenon of compounding is ubiquitous in Sanskrit.
Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
This data also can be used for a code-mixed machine translation task.
Cultural Vocal Bursts Intensity Prediction
Machine Translation
+1
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing
While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies.
Evaluating Neural Word Embeddings for Sanskrit
To effectively use such readily available resources, it is very much essential to perform a systematic study on word embedding approaches for the Sanskrit language.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting.
Neural Approaches for Data Driven Dependency Parsing in Sanskrit
We compare the performance of each of the models in a low-resource setting, with 1, 500 sentences for training.
