Papers by Charu Sharma
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
| Outcome: | The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations. |
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. |
An Unsupervised, Geometric and Syntax-aware Quantification of Polysemy (2022.emnlp-main)
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| Challenge: | Polysemy is the phenomenon where a single word form possesses two or more related senses. |
| Approach: | They propose an unsupervised framework to quantify polysemy for words in multiple languages . they use syntactic knowledge to infuse the framework with syntaktic knowledge . |
| Outcome: | The proposed framework is based on syntactic knowledge and is compared with existing methods in English, French and Spanish. |