Ananiadou, S. - NaCTeM Publications

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Vorname(n):	S.
Nachname(n):	Ananiadou

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Liu, B, Schlegel, V., Batista-Navarro, R. und Ananiadou, S., Entity Coreference and Co-occurrence Aware Argument Mining from Biomedical Literature, in: Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023, 2023

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Liu, B, Schlegel, V., Batista-Navarro, R. und Ananiadou, S., Argument mining as a multi-hop generative machine reading comprehension task, in: Findings of the Association for Computational Linguistics: EMNLP 2023, Seiten 10846–10858, 2023

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Liu, B, Schlegel, V., Batista-Navarro, R. und Ananiadou, S., Incorporating Zoning Information into Argument Mining from Biomedical Literature, in: Proceedings of LREC 2022, Seiten 6162-6169, 2022

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Liu, B, Schlegel, V., Thompson, P., Batista-Navarro, R. und Ananiadou, S., Global information-aware argument mining based on a top-down multi-turn QA model (2023), in: Information Processing & Management, 60:5(103445)

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Liu, Z., Liu, B, Thompson, P., Yang, K und Ananiadou, S., 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

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Liu, Z., Qian, L., Xie, Q., Huang, J., Yang, K und Ananiadou, S., MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs, arXiv, 2025

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Liu, Z., Thompson, P., Rong, J. und Ananiadou, S., ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories, arXiv, 2025

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Liu, Z., Wang, K., Bao, Z., Zhang, X., Dong, J., Yang, K, Kabir, M., Giannouris, P., Xing, R., Park, S., Kim, J., Li, D., Xie, Q. und Ananiadou, S., FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task, in: Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), Seiten 271–276, 2025

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Liu, Z., Yang, K, Hovy, E. und Ananiadou, S., Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments, arXiv, 2025

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Liu, Z., Yang, K, Xie, Q., de Kock, C., Ananiadou, S. und Hovy, E., RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information, in: Proceedings of ACL 2025, In Press

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Liu, Z., Yang, K, Zhang, T., Xie, Q., Yu, Z. und Ananiadou, S., EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis, in: Proceedings of KDD 2024, Seiten 5487 - 5496, 2024

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Liu, Z., Zhang, T., Yang, K, Thompson, P., Yu, Z. und Ananiadou, S., Emotion detection for misinformation: A review (2024), in: Information Fusion, 107(102300)

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Liu, Z., Zhang, X., Yang, K, Xie, Q., Huang, J. und Ananiadou, S., FMDLlama: Financial Misinformation Detection based on Large Language Models, arXiv, 2024

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Luo, Z., Liu, L., Ananiadou, S. und Xie, Q., Graph Contrastive Topic Model (2024), in: Expert Systems with Applications, 255:Part C(124631)

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Luo, Z., Xie, Q. und Ananiadou, S., The Lay Person’s Guide to Biomedicine: Orchestrating Large Language Models, arXiv, 2024

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Luo, Z., Xie, Q. und Ananiadou, S., Factual consistency evaluation of summarization in the Era of large language models (2024), in: Expert Systems with Applications, 254(124456)

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Luo, Z., Xie, Q. und Ananiadou, S., CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization, in: Proceedings of the ACM Web Conference, Seiten 1843–1852, 2023

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Luo, Z., Xie, Q. und Ananiadou, S., ChatGPT as a Factual Inconsistency Evaluator for Text Summarization, arXiv, 2023

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Luo, Z., Xie, Q. und Ananiadou, S., Readability Controllable Biomedical Document Summarization, in: Findings of the Association for Computational Linguistics: EMNLP 2022, Seiten 4667–4680, 2022

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Luo, Z., Yuan, C., Xie, Q. und Ananiadou, S., EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions, in: Findings of the Association for Computational Linguistics: ACL 2025, In Press

Luo, Z., Yuan, C., Xie, Q. und Ananiadou, S., Are Large Language Models True Healthcare Jacks-of-All-Trades? Benchmarking Across Health Professions Beyond Physician Exams, arXiv, 2024

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Lyte, V., Jones, S., Ananiadou, S. und Kerr, L., UK Institutional Repository Search: Innovation and Discovery (2009), in: Ariadne, 61

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Matsuoka, Y., Ghosh, S., Kikuchi, N., SÃ¦tre, R., Kemper, B., Okazaki, N., Ananiadou, S., Tsujii, J. und Kitano, H., Community Platform for Pathway Model Building, in: 10th International Conference on Systems Biology, Seiten 139-140, Stanford University, 2009

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McEntyre, J. R., Ananiadou, S., Andrews, S., Black, W.J., Boulderstone, R., Buttery, P., Chaplin, D., Chevuru, S., Cobley, N., Coleman, L., Davey, P., Gupta, B., Haji-Gholam, L., Hawkins, C., Horne, A., Hubbard, S. J., Kim, J. -H., Lewin, I., Lyte, V., MacIntyre, R., Mansoor, S., Mason, L., McNaught, J., Newbold, E., Nobata, C., Ong, E., Pillai, S., Rebholz-Schuhmann, D., Rosie, H., Rowbotham, R., Rupp, C. J., Stoehr, P. und Vaughan, P., UKPMC: a full text article resource for the life sciences (2010), in: Nucleic Acids Research, 39:Suppl. 1(D58-D65)

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Meziane, F., Athanasakis, N. und Ananiadou, S., Generating Natural Language specifications from UML class diagrams (2008), in: Requirements Engineering, 13:1(1--18)

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