Ahmed Elsayed

Also published as: Ahmed ELsayed


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COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang | Manling Li | Xuan Wang | Nikolaus Parulian | Guangxing Han | Jiawei Ma | Jingxuan Tu | Ying Lin | Ranran Haoran Zhang | Weili Liu | Aabhas Chauhan | Yingjun Guan | Bangzheng Li | Ruisong Li | Xiangchen Song | Yi Fung | Heng Ji | Jiawei Han | Shih-Fu Chang | James Pustejovsky | Jasmine Rah | David Liem | Ahmed ELsayed | Martha Palmer | Clare Voss | Cynthia Schneider | Boyan Onyshkevych
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.

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Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation
Zixuan Zhang | Nikolaus Parulian | Heng Ji | Ahmed Elsayed | Skatje Myers | Martha Palmer
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model’s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.


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SemEval 2018 Task 6: Parsing Time Normalizations
Egoitz Laparra | Dongfang Xu | Ahmed Elsayed | Steven Bethard | Martha Palmer
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs of the Semantically Compositional Annotation of Time Expressions (SCATE) schema, which allows the representation of a wider variety of time expressions than previous approaches. Two tracks were included, one to evaluate the parsing of individual components of the produced graphs, in a classic information extraction way, and another one to evaluate the quality of the time intervals resulting from the interpretation of those graphs. Though 40 participants registered for the task, only one team submitted output, achieving 0.55 F1 in Track 1 (parsing) and 0.70 F1 in Track 2 (intervals).