Yang, K - NaCTeM Publications - Aigaion 2.0

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Vorname(n):	K
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Yang, K, Zhang, T., Alhuzali, H. und Ananiadou, S., Cluster-Level Contrastive Learning for Emotion Recognition in Conversations (2023), in: IEEE Transactions on Affective Computing(1-12)

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Yang, K, Zhang, T. und Ananiadou, S., Disentangled Variational Autoencoder for Emotion Recognition in Conversations (2023), in: IEEE Transactions on Affective Computing(1-12)

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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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Zhang, T., Yang, K, Shaoxiong, J. und Ananiadou, S., Emotion fusion for mental illness detection from social media: A survey (2023), in: Information Fusion, 92(231-246)

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Zhang, T., Yang, K, Alhuzali, H., Liu, B und Ananiadou, S., PHQ-aware depressive symptoms identification with similarity contrastive learning on social media (2023), in: Information Processing & Management(103417)

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Yang, K, Zhang, T. und Ananiadou, S., A Mental State Knowledge-Aware and Contrastive Network for Early Stress and Depression Detection on Social Media (2022), in: Information Processing and Management, 59:4(102961)

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Yang, K, Zhang, T., Kuang, Z., Xie, Q., Huang, J. und Ananiadou, S., MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models, in: Proceedings of WWW 2024, Seiten 4489 - 4500, 2024

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Zhang, T., Yang, K, Ji, S., Liu, B, Xie, Q. und Ananiadou, S., SuicidEmoji: Derived Emoji Dataset and Tasks for Suicide-Related Social Content, in: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), Seiten 1136 - 1141, 2024

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Yang, K, Zhang, T. und Ananiadou, S., A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge, in: Proceedings of CIKM 2023, Seiten 2917–2927, 2023

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Zhang, T., Yang, K und Ananiadou, S., Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media, in: Proceedings of BioNLP 2023, Seiten 114–126, 2023

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Yang, K, Ji, S., Zhang, T., Xie, Q., Kuang, Z. und Ananiadou, S., Towards Interpretable Mental Health Analysis with Large Language Models, in: Proceedings of EMNLP 2023, Seiten 6056–6077, 2023

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Liu, Z., Liu, B, Thompson, P., Yang, K, Jain, R. und Ananiadou, S., ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model, arXiv, 2024

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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, arXiv, 2024

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Hua, Y., Liu, F., Yang, K, Li, Z., Sheu, Y., Zhou, P., Moran, L. V., Ananiadou, S. und Beam, A., Large Language Models in Mental Health Care: a Scoping Review, arXiv, 2024

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Ji, S., Zhang, T., Yang, K und Ananiadou, S., Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health, arXiv, 2023

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Yu, Z., Yang, K, Liu, Z. und Ananiadou, S., Locating Factual Knowledge in Large Language Models: Exploring the Residual Stream and Analyzing Subvalues in Vocabulary Space, arXiv, 2023

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Shaoxiong, J., Zhang, T., Yang, K und Ananiadou, S., Rethinking large language models in mental health applications, arXiv, 2023

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