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Social Media Conversation Monitoring: Visualize Information Contents of Twitter Messages Using Conversational Metrics.

, , , и . ICCS, том 80 из Procedia Computer Science, стр. 2216-2220. Elsevier, (2016)

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Lothar Carlowitz

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Pheonix at SemEval-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling., , , и . SemEval@COLING, стр. 677-682. International Committee for Computational Linguistics, (2020)Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations., , , и . Comput. Hum. Behav., (2016)WINS: Web Interface for Network Science via Natural Language Distributed Representations., , , , , , , , и . HCI (38), том 1224 из Communications in Computer and Information Science, стр. 614-621. Springer, (2020)Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification., и . CoRR, (2024)Mitigating Large Language Model Bias: Automated Dataset Augmentation and Prejudice Quantification., и . Comput., 13 (6): 141 (июня 2024)Tell me the truth: A system to measure the trustworthiness of Large Language Models.. CoRR, (2024)Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison., и . CoRR, (2024)Social Media Conversation Monitoring: Visualize Information Contents of Twitter Messages Using Conversational Metrics., , , и . ICCS, том 80 из Procedia Computer Science, стр. 2216-2220. Elsevier, (2016)Measuring Polarization in Twitter Enabled in Online Political Conversation: The Case of 2016 US Presidential Election., , , , и . IRI, стр. 607-613. IEEE Computer Society, (2017)From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis., , , , и . CloudNet, стр. 229-237. IEEE, (2023)