1 code implementation • 14 Oct 2023 • Jivnesh Sandhan, Yaswanth Narsupalli, Sreevatsa Muppirala, Sriram Krishnan, Pavankumar Satuluri, Amba Kulkarni, Pawan Goyal
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.
no code implementations • 17 Aug 2023 • Jivnesh Sandhan
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.
1 code implementation • 14 Aug 2023 • Jivnesh Sandhan, Amruta Barbadikar, Malay Maity, Pavankumar Satuluri, Tushar Sandhan, Ravi M. Gupta, Pawan Goyal, Laxmidhar Behera
We provide a deep analysis of Siksastaka, a Sanskrit poem, from the perspective of 6 prominent kavyashastra schools, to illustrate the proposed framework.
1 code implementation • 19 Feb 2023 • Jivnesh Sandhan, Anshul Agarwal, Laxmidhar Behera, Tushar Sandhan, Pawan Goyal
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.
1 code implementation • 21 Oct 2022 • Jivnesh Sandhan, Rathin Singha, Narein Rao, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
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.
1 code implementation • COLING 2022 • Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
The phenomenon of compounding is ubiquitous in Sanskrit.
1 code implementation • LaTeCHCLfL (COLING) 2022 • Jivnesh Sandhan, Ayush Daksh, Om Adideva Paranjay, Laxmidhar Behera, Pawan Goyal
This data also can be used for a code-mixed machine translation task.
Cultural Vocal Bursts Intensity Prediction Machine Translation +1
1 code implementation • 27 Jan 2022 • Jivnesh Sandhan, Laxmidhar Behera, Pawan Goyal
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.
1 code implementation • 1 Apr 2021 • Jivnesh Sandhan, Om Adideva, Digumarthi Komal, Laxmidhar Behera, Pawan Goyal
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.
1 code implementation • EACL 2021 • Jivnesh Sandhan, Amrith Krishna, Ashim Gupta, Laxmidhar Behera, Pawan Goyal
In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting.
no code implementations • 17 Apr 2020 • Amrith Krishna, Ashim Gupta, Deepak Garasangi, Jivnesh Sandhan, Pavankumar Satuluri, Pawan Goyal
We compare the performance of each of the models in a low-resource setting, with 1, 500 sentences for training.