Papers by Karim Ghonim
RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Entity Linking and Entity Disambiguation systems assume static knowledge bases are incomplete and up-to-date, rendering them incapable of handling entities not yet included in the knowledge base. |
| Approach: | They propose a model that retrieves external knowledge to improve factual grounding in entity descriptions. |
| Outcome: | The proposed model outperforms systems that require fixed knowledge sets on Entity Disambiguation and Wikipedia to improve factual grounding in entity descriptions. |
When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)
Copied to clipboard
| Challenge: | Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment. |
| Approach: | They propose a pipeline for generating multilingual conversational NER datasets with minimal human validation and a framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data. |
| Outcome: | The proposed framework outperforms existing models on public and private conversations by 97.12% on CoNLL-2003 and 83.09% on OntoNotes 5.0. |
Concept-pedia: a Wide-coverage Semantically-annotated Multimodal Dataset (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current evaluations for Vision-language Models remain heavily anchored to ImageNet . |
| Approach: | They propose a large-scale semantically-annotated multimodal resource that extends the range of visual concepts, including diverse abstract categories. |
| Outcome: | The proposed model expands the range of visual concepts, including diverse abstract categories. |
FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in text summarization have shown remarkable performance, but a significant number of summaries exhibit factual inconsistencies, such as hallucinations. |
| Approach: | They propose a factuality-oriented metric that evaluates text summarization for accuracy . they use a human annotation process to examine the accuracy of automatically generated summaries . |
| Outcome: | The proposed metric sets a new state-of-the-art on AGGREFACT, the de-facto benchmark for factuality evaluation. |
Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)
Copied to clipboard
Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli
| Challenge: | Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications. |
| Approach: | They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task. |
| Outcome: | The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language. |