Papers by Niharika Niharika
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)
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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler
| Challenge: | Named entity recognition is a technique for extracting information from unstructured data with known labels. |
| Approach: | They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases . |
| Outcome: | The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods . |
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
| Approach: | They propose a framework for multimodal deepfake detection that uses contrastive learning and large language models to mitigate modality fragmentation and refine embeddings to address shallow inter-modal reasoning. |
| Outcome: | ConLLM reduces audio deepfake EER by 50%, improves video accuracy by 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. |
JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks (2023.findings-eacl)
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Nidhi Goyal, Jushaan Kalra, Charu Sharma, Raghava Mutharaju, Niharika Sachdeva, Ponnurangam Kumaraguru
| Challenge: | Existing approaches to predict missing skills are limited to contextual modelling and do not exploit inter-relational structures like job-job and job-skill relationships. |
| Approach: | They propose a skill prediction framework that exploits structural relationships to predict missing skills using job descriptions. |
| Outcome: | The proposed framework outperforms the state-of-the-art approaches by 6% in precision and 3% in recall on real-world recruitment datasets. |
DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting (2026.acl-industry)
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Md Mofijul Islam, Md Sirajus Salekin, Nivedha Balakrishnan, Vincil C. Bishop III, Niharika Jain, Spencer Romo, Bob Strahan, Boyi Xie, Diego A. Socolinsky
| Challenge: | Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. |
| Approach: | They propose to use document packet splitting to improve document understanding in real-world applications. |
| Outcome: | The proposed datasets and evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. |