Kaustubh Hiware


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NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
Kaustubh Hiware | Ritam Dutt | Sayan Sinha | Sohan Patro | Kripa Ghosh | Saptarshi Ghosh
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario – identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching. To this end, we present NARMADA, a semi-automated platform which leverages the crowd-sourced information from social media posts for assisting post-disaster relief coordination efforts. The system employs Natural Language Processing and Information Retrieval techniques for identifying resource-needs and resource-availabilities from microblogs, extracting resources from the posts, and also matching the needs to suitable availabilities. The system is thus capable of facilitating the judicious management of resources during post-disaster relief operations.


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A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit
Amrith Krishna | Pavankumar Satuluri | Harshavardhan Ponnada | Muneeb Ahmed | Gulab Arora | Kaustubh Hiware | Pawan Goyal
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing

Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.