Ananiadou, S. - NaCTeM Publications

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First name(s):	S.
Last name(s):	Ananiadou

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

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

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Liu, B, Schlegel, V., Thompson, P., Batista-Navarro, R. and 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 and 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 and 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. and 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. and 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), pages 271–276, 2025

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Liu, Z., Yang, K, Hovy, E. and 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. and 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. and Ananiadou, S., EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis, in: Proceedings of KDD 2024, pages 5487 - 5496, 2024

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Liu, Z., Zhang, T., Yang, K, Thompson, P., Yu, Z. and 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. and Ananiadou, S., FMDLlama: Financial Misinformation Detection based on Large Language Models, arXiv, 2024

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

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

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

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

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

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Luo, Z., Yuan, C., Xie, Q. and 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. and 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. and 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. and Kitano, H., Community Platform for Pathway Model Building, in: 10th International Conference on Systems Biology, pages 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. and 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. and Ananiadou, S., Generating Natural Language specifications from UML class diagrams (2008), in: Requirements Engineering, 13:1(1--18)

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