Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
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 fine-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 fine-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 difficult 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.
Code (1)
Tasks
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERSimilar Papers 제목 키워드 기반
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
A major problem in road network analysis is discovery of important crossroads, which can provide useful information for transport planning. However, none of existing approaches addresses the problem of identifying networ…
FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchronized GANs
Coarse building mass models are now routinely generated at scales ranging from individual buildings through to whole cities. For example, they can be abstracted from raw measurements, generated procedurally, or created m…
Texture SynthesisFine-grained Urban Flow Inference with Incomplete Data
Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city given the coarse-grained urban flow observations, is critically important to various smart city related applications such as u…
Fine-Grained Urban Flow InferenceMulti-Task LearningSuper-ResolutionUB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images
Fine classification of city-scale buildings from satellite remote sensing imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analy…
ClassificationDenoisingKnowledge DistillationSuper-ResolutionPredicting building types and functions at transnational scale
Building-specific knowledge such as building type and function information is important for numerous energy applications. However, comprehensive datasets containing this information for individual households are missing …
Graph Neural Network