Papers by Nidhi Goyal
AttriSage: Product Attribute Value Extraction Using Graph Neural Networks (2024.eacl-srw)
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| Challenge: | Existing methods for extracting attribute value from product descriptions are limited in their accuracy. |
| Approach: | They propose a method for extracting product attribute value from product description using graphs and neural networks. |
| Outcome: | The proposed method improves product description attribute value extraction accuracy compared to baseline methods. |
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. |