4 research outputs found
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
The primary focus of this thesis is to make Sanskrit manuscripts more
accessible to the end-users through natural language technologies. The
morphological richness, compounding, free word orderliness, and low-resource
nature of Sanskrit pose significant challenges for developing deep learning
solutions. 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. The first task, Sanskrit
Word Segmentation (SWS), is a fundamental text processing task for any other
downstream applications. However, it is challenging due to the sandhi
phenomenon that modifies characters at word boundaries. Similarly, the existing
dependency parsing approaches struggle with morphologically rich and
low-resource languages like Sanskrit. Compound type identification is also
challenging for Sanskrit due to the context-sensitive semantic relation between
components. All these challenges result in sub-optimal performance in NLP
applications like question answering and machine translation. Finally, Sanskrit
poetry has not been extensively studied in computational linguistics.
While addressing these challenges, this thesis makes various contributions:
(1) The thesis proposes linguistically-informed neural architectures for these
tasks. (2) We showcase the interpretability and multilingual extension of the
proposed systems. (3) Our proposed systems report state-of-the-art performance.
(4) Finally, we present a neural toolkit named SanskritShala, a web-based
application that provides real-time analysis of input for various NLP tasks.
Overall, this thesis contributes to making Sanskrit manuscripts more accessible
by developing robust NLP technology and releasing various resources, datasets,
and web-based toolkit.Comment: Ph.D. dissertatio
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. Our systems currently
report state-of-the-art performance on available benchmark datasets for all
tasks. SanskritShala is deployed as a web-based application, which allows a
user to get real-time analysis for the given input. It is built with
easy-to-use interactive data annotation features that allow annotators to
correct the system predictions when it makes mistakes. We publicly release the
source codes of the 4 modules included in the toolkit, 7 word embedding models
that have been trained on publicly available Sanskrit corpora and multiple
annotated datasets such as word similarity, relatedness, categorization,
analogy prediction to assess intrinsic properties of word embeddings. So far as
we know, this is the first neural-based Sanskrit NLP toolkit that has a
web-based interface and a number of NLP modules. We are sure that the people
who are willing to work with Sanskrit will find it useful for pedagogical and
annotative purposes. SanskritShala is available at:
https://cnerg.iitkgp.ac.in/sanskritshala. The demo video of our platform can be
accessed at: https://youtu.be/x0X31Y9k0mw4.Comment: 7 pages, Accepted at ACL23 (Demo track) to be held at Toronto, Canad
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 and
cultural landscape of the Indian subcontinent for centuries. However, not much
attention has been devoted to uncovering the hidden beauty of Sanskrit poetry
in computational linguistics. This article explores the intersection of
Sanskrit poetry and computational linguistics by proposing a roadmap of an
interpretable framework to analyze and classify the qualities and
characteristics of fine Sanskrit poetry. We discuss the rich tradition of
Sanskrit poetry and the significance of computational linguistics in
automatically identifying the characteristics of fine poetry. The proposed
framework involves a human-in-the-loop approach that combines deterministic
aspects delegated to machines and deep semantics left to human experts. We
provide a deep analysis of Siksastaka, a Sanskrit poem, from the perspective of
6 prominent kavyashastra schools, to illustrate the proposed framework.
Additionally, we provide compound, dependency, anvaya (prose order linearised
form), meter, rasa (mood), alankar (figure of speech), and riti (writing style)
annotations for Siksastaka and a web application to illustrate the poem's
analysis and annotations. Our key contributions include the proposed framework,
the analysis of Siksastaka, the annotations and the web application for future
research. Link for interactive analysis:
https://sanskritshala.github.io/shikshastakam/Comment: 15 page