General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.But many applications require identifying fne-grained locations from texts and mapping them precisely to geographic sites, e.g., a crossroad, an apartment building, or a grocery store.In this paper, we introduce a new dataset HarveyNER with fne-grained locations annotated in tweets.This dataset presents unique challenges and characterizes many complex and long location mentions in informal descriptions.We built strong baseline models using Curriculum Learning and experimented with different heuristic curricula to better recognize diffcult location mentions.Experimental results show that the simple curricula can improve the system's performance on hard cases and its overall performance, and outperform several other baseline systems.The dataset and the baseline models can be found at https: //github.com/brickee/HarveyNER.

People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.