通过文本分析实时推断灾难微博客位置，无需地理标签。
GeoSense-AI: Fast Location Inference from Crisis Microblogs
- 结合词性标注与依赖解析，精准识别灾害相关专有名词。
- 相比主流工具，速度提升数个量级，保持高准确率。
- 适合应急响应、实时舆情监控等需要快速定位的场景。
本文提出一种用于从嘈杂微博客流中实时推断地理位置的应用型AI流水线，整合了统计哈希标签分割、基于词性标注的专有名词检测、围绕灾害词汇的依存解析、轻量化命名实体识别及地名知识库引导的消歧技术，直接从文本中推断位置，而非依赖稀疏的地理标签。该方法在流式处理约束下实现信息抽取，强调低延迟NLP组件与高效地理知识库验证，以支持紧急情况下的态势感知。与广泛使用的命名实体识别工具包相比，系统在保持高F1值的同时，具备数量级更快的吞吐能力，适用于实时危机情报场景。一个生产级地图界面展示了从数据摄入、推理到可视化的全流程功能，可大规模揭示洪水、疫情等快速演变事件中的位置信号。通过强化对非正式文本的鲁棒性与流式效率，GeoSense-AI展示出领域定制化NLP与知识锚定如何超越传统地理标签依赖，提升应急响应能力。
原文摘要 · Abstract (English)
This paper presents an applied AI pipeline for realtime 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 geotags. 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 fastmoving 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.
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