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This research advances real-time crisis informatics by deploying a low-latency NLP pipeline that extracts geolocation data directly from noisy microblog streams, reducing reliance on sparse geo-tags. The system achieves high accuracy through domain-tuned named-entity recognition and gazetteer-grounded disambiguation, enabling rapid situational awareness for floods and outbreaks.
GeoSense-AI: Fast Location Inference from Crisis Microblogs
arXiv:2512.18225v2 Announce Type: replace Abstract: This paper presents an applied AI pipeline for real-time geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than sparse geo-tags. The approach operationalizes information extraction under streaming constraints, emphasizing low-latency NLP components and efficient validation against geographic knowledge bases to support situational awareness during emergencies. In head-to-head comparisons with widely used NER toolkits, the system attains strong F1 while being engineered for orders-of-magnitude faster throughput, enabling deployment in live crisis informatics settings. A production map interface demonstrates end-to-end AI functionality---ingest, inference, and visualization---surfacing locational signals at scale for floods, outbreaks, and other fast-moving events. By prioritizing robustness to informal text and streaming efficiency, GeoSense-AI illustrates how domain-tuned NLP and knowledge grounding can elevate emergency response beyond conventional geo-tag reliance.
Abstract:This paper presents an applied AI pipeline for real-time geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than sparse geo-tags. The approach operationalizes information extraction under streaming constraints, emphasizing low-latency NLP components and efficient validation against geographic knowledge bases to support situational awareness during emergencies. In head-to-head comparisons with widely used NER toolkits, the system attains strong F1 while being engineered for orders-of-magnitude faster throughput, enabling deployment in live crisis informatics settings. A production map interface demonstrates end-to-end AI functionality---ingest, inference, and visualization---surfacing locational signals at scale for floods, outbreaks, and other fast-moving events. By prioritizing robustness to informal text and streaming efficiency, GeoSense-AI illustrates how domain-tuned NLP and knowledge grounding can elevate emergency response beyond conventional geo-tag reliance.
Computation and Language (cs.CL); Social and Information Networks (cs.SI)
arXiv:2512.18225 [cs.CL]
(or arXiv:2512.18225v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2512.18225 Focus to learn more arXiv-issued DOI via DataCite
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