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arXiv Machine Learning By Aijaz Ali, Nazish Basir, Sarfaraz Nawaz, Danish Nazir Arain, Haris Ali

HUMAID-NER: A Disaster Tweet Dataset for Joint Named Entity Recognition and Event Classification via Uncertainty-Weighted Multitask Learning

Read the original on arXiv Machine Learning →

HUMAID-NER is the first named entity recognition dataset built on the HumAID benchmark, comprising 60,000 English disaster tweets with approximately 175,000 labeled entity spans across ten operationally motivated entity types. The dataset was created using a reproducible three‑stage hybrid pipeline that combines a spaCy transformer model, disaster‑domain EntityRuler patterns, and structured regular expressions with priority‑based overlap resolution. A joint multitask learning framework using a shared RoBERTa‑large encoder and homoscedastic uncertainty weighting achieves an NER span micro‑F1 of 0.841 and classification macro‑F1 of 0.761, and the authors provide a real‑time web dashboard, dataset, models, and pipeline code for reproducibility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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