no code implementations • 22 Feb 2024 • Ashish Kumar, Laxmidhar Behera
Autonomous aerial harvesting is a highly complex problem because it requires numerous interdisciplinary algorithms to be executed on mini low-powered computing devices.
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 • 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 • 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 • 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 • 16 Jan 2021 • Ashish Kumar, Laxmidhar Behera
The primary motivation behind this work is the limitation of the traditional loss functions for unsupervised learning of a given task.
no code implementations • 11 Oct 2020 • Anima Majumder, Samrat Dutta, Swagat Kumar, Laxmidhar Behera
This is achieved by finding \emph{no man's land} based on Euclidean distance between the feature vectors.
no code implementations • 9 Jan 2020 • Siddhartha Vibhu Pharswan, Mohit Vohra, Ashish Kumar, Laxmidhar Behera
In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions.
no code implementations • 3 Jan 2020 • Mohit Vohra, Ravi Prakash, Laxmidhar Behera
In this paper, a real-time grasp pose estimation strategy for novel objects in robotic pick and place applications is proposed.
Robotics Image and Video Processing
no code implementations • 27 Apr 2015 • Vipul Arora, Laxmidhar Behera, Ajay Pratap Yadav
It is hoped that the proposed algorithm will bring in a lot of interest in researchers working in developing fast learning algorithms and global optimization.