Amit Agarwal
2024
IITRoorkee@SMM4H 2024 Cross-Platform Age Detection in Twitter and Reddit Using Transformer-Based Model
Thadavarthi Sankar
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Dudekula Suraj
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Mallamgari Reddy
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Durga Toshniwal
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Amit Agarwal
Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
This paper outlines the methodology for the automatic extraction of self-reported ages from social media posts as part of the Social Media Mining for Health (SMM4H) 2024 Workshop Shared Tasks. The focus was on Task 6: “Self-reported exact age classification with cross-platform evaluation in English.” The goal was to accurately identify age-related information from user-generated content, which is crucial for applications in public health monitoring, targeted advertising, and demographic research. A number of transformer-based models were employed, including RoBERTa-Base, BERT-Base, BiLSTM, and Flan T5 Base, leveraging their advanced capabilities in natural language understanding. The training strategies included fine-tuning foundational pre-trained language models and evaluating model performance using standard metrics: F1-score, Precision, and Recall. The experimental results demonstrated that the RoBERTa-Base model significantly outperformed the other models in this classification task. The best results achieved with the RoBERTa-Base model were an F1-score of 0.878, a Precision of 0.899, and a Recall of 0.858.
AgriLLM:Harnessing Transformers for Framer Queries
Krish Didwania
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Pratinav Seth
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Aditya Kasliwal
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Amit Agarwal
Proceedings of the Third Workshop on NLP for Positive Impact
Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information.The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps.Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture.This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types.
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Co-authors
- Thadavarthi Sankar 1
- Dudekula Suraj 1
- Mallamgari Reddy 1
- Durga Toshniwal 1
- Krish Didwania 1
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