Papers by Kuldeep Singh
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text (N19-1)
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Ahmad Sakor, Isaiah Onando Mulang’, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, Sören Auer
| Challenge: | Short texts challenge NLP tasks because they lack context or are partially malformed. |
| Approach: | They propose a method which maps entities and relations within a short text to Wikipedia mentions. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for short text query inventories. |
Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)
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| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
| Approach: | They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document. |
| Outcome: | The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)
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Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, Jens Lehmann
| Challenge: | Existing approaches to target end-to-end entity linking over knowledge bases are not efficient. |
| Approach: | They propose a modular approach to target end-to-end entity linking over knowledge bases. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases. |
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks (2021.eacl-main)
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| Challenge: | Existing knowledge graphs are widely used for (complex) conversational question answering . LASAGNE improves the F1-score on eight out of ten question types . |
| Approach: | They propose a multi-task neural semantic parsing approach for (complex) conversational question answering over a knowledge graph using a transformer model and a Graph Attention Networks model. |
| Outcome: | The proposed approach outperforms baselines on eight out of ten question types on a standard dataset for complex sequential question answering. |
UnityAI Guard: Pioneering Toxicity Detection Across Low-Resource Indian Languages (2025.emnlp-demos)
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Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain, Pavan Deekshith Doddi, Eshwar Dhande, Adithya Ananth, null Kuldeep, Mayank Singh
| Challenge: | Existing systems target high-resource languages, but UnityAI-Guard addresses this gap by developing state-of-the-art models for binary toxicity classification targeting low-resourced Indian languages. |
| Approach: | They propose a framework for binary toxicity classification targeting low-resource Indian languages. |
| Outcome: | The proposed framework achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. |