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Publications of Ananiadou, S. sorted by first author
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ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model, in: Proceedings of the 13th International Conference on Prestigious Applications of Intelligent Systems (PAIS-2024), 2024 | , , , and ,
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RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information, arXiv, 2024 | , , , , and ,
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EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis, in: Proceedings of KDD 2024, pages 5487 - 5496, 2024 | , , , , and ,
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Emotion detection for misinformation: A review (2024), in: Information Fusion, 107(102300) | , , , , and ,
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FMDLlama: Financial Misinformation Detection based on Large Language Models, arXiv, 2024 | , , , , and ,
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Graph Contrastive Topic Model (2024), in: Expert Systems with Applications, 255:Part C(124631) | , , and ,
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The Lay Person’s Guide to Biomedicine: Orchestrating Large Language Models, arXiv, 2024 | , and ,
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Factual consistency evaluation of summarization in the Era of large language models (2024), in: Expert Systems with Applications, 254(124456) | , and ,
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CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization, in: Proceedings of the ACM Web Conference, pages 1843–1852, 2023 | , and ,
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ChatGPT as a Factual Inconsistency Evaluator for Text Summarization, arXiv, 2023 | , and ,
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Readability Controllable Biomedical Document Summarization, in: Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4667–4680, 2022 | , and ,
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Are Large Language Models True Healthcare Jacks-of-All-Trades? Benchmarking Across Health Professions Beyond Physician Exams, arXiv, 2024 | , , and ,
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UK Institutional Repository Search: Innovation and Discovery (2009), in: Ariadne, 61 | , , and ,
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Community Platform for Pathway Model Building, in: 10th International Conference on Systems Biology, pages 139-140, Stanford University, 2009 | , , , , , , , and ,
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UKPMC: a full text article resource for the life sciences (2010), in: Nucleic Acids Research, 39:Suppl. 1(D58-D65) | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and ,
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Generating Natural Language specifications from UML class diagrams (2008), in: Requirements Engineering, 13:1(1--18) | , and ,
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The Meta-knowledge of Causality in Biomedical Scientific Discourse, in: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland, pages 1984-1991, European Language Resources Association (ELRA), 2014 | and ,
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Semi-supervised learning of causal relations in biomedical scientific discourse (2014), in: BioMedical Engineering OnLine, 13:Suppl 2(S1) | and ,
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What causes a causal relation? Detecting causal triggers in biomedical scientific discourse, in: 51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop, Sofia, Bulgaria, pages 38-45, Association for Computational Linguistics, 2013 | and ,
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Recognising Discourse Causality Triggers in the Biomedical Domain (2013), in: Journal of Bioinformatics and Computational Biology, 11:6(1343008) | and ,
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A Hybrid Approach to Recognising Discourse Causality in the Biomedical Domain, in: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2013, Shanghai, China, pages 361-366, IEEE, 2013 | and ,
Mining the biomedical literature, in: Healthcare Data Analytics, pages 251-308, CRC Press, 2015 | , , , , , , and ,
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Analysing Entity Type Variation across Biomedical Subdomains, in: Proceedings of the Third Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2012), Istanbul, Turkey, pages 1-7, 2012 | , and ,
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Towards a Better Understanding of Discourse: Integrating Multiple Discourse Annotation Perspectives Using UIMA, in: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, Association for Computational Linguistics, Sofia, Bulgaria, pages 79-88, 2013 | , , , , and ,
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BioCause: Annotating and Analysing Causality in the Biomedical Domain (2013), in: BMC Bioinformatics, 14:1(2) | , , and ,
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| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-407 |