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Preprints
, 2026, CityLLM: A framework for natural-language querying of semantic 3D city models, https://arxiv.org/abs/2607.14542v1
, 2026, IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations, http://dx.doi.org/10.48550/arxiv.2605.13236
, 2025, Langformers: Unified NLP Pipelines for Language Models, https://arxiv.org/abs/2504.09170v1
, 2025, "Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts, https://arxiv.org/abs/2502.16839v1
, 2024, CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media, https://arxiv.org/abs/2405.11897v2
, 2024, Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts, https://arxiv.org/abs/2403.16614v1
, 2023, CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts, https://doi.org/10.1016/j.knosys.2024.111916
, 2023, A Twitter narrative of the COVID-19 pandemic in Australia, https://doi.org/10.59297/GQED8281
, 2023, BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration, https://doi.org/10.1016/j.dib.2023.109229
, 2023, GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global Geotagged Twitter Conversations, https://arxiv.org/abs/2301.07378v1
, 2022, Where did you tweet from? Inferring the origin locations of tweets based on contextual information, https://doi.org/10.1109/BigData55660.2022.10020460
, 2022, Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey, https://doi.org/10.1145/3524498
, 2022, Twitter conversations predict the daily confirmed COVID-19 cases, https://doi.org/10.1016/j.asoc.2022.109603
, 2018, Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity, https://doi.org/10.1007/s42452-020-03582-5
, 2018, Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning, https://arxiv.org/abs/1809.09813v5