Geocoding is the task of converting location mentions in text into structured
data that encodes the geospatial semantics. We propose a new architecture for
geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to
generate a list of candidate entries from the geospatial ontology. Then it
reranks the candidate entries using a transformer-based neural network that
incorporates information from the ontology such as the entry's population. This
generate-and-rerank process is applied twice: first to resolve the less
ambiguous countries, states, and counties, and second to resolve the remaining
location mentions, using the identified countries, states, and counties as
context. Our proposed toponym resolution framework achieves state-of-the-art
performance on multiple datasets. Code and models are available at
https://github.com/clulab/geonorm.

GeoNorm 是一种新的地理编码体系结构，它使用了信息检索技术来生成地理空间本体库中的候选条目列表，然后使用基于变压器的神经网络重新整理这些条目，该神经网络融合了本体库的信息，例如条目的人口。我们提出的地名解析框架在多个数据集上实现了最先进的性能。

通过更好的候选生成和基于 Transformer 的重新排序以及两阶段解析提高地名解析能力

Improving Toponym Resolution with Better Candidate Generation,  Transformer-based Reranking, and Two-Stage Resolution

Text data are an important source of detailed information about social and
political events. Automated systems parse large volumes of text data to infer
or extract structured information that describes actors, actions, dates, times,
and locations. One of these sub-tasks is geocoding: predicting the geographic
coordinates associated with events or locations described by a given text. We
present an end-to-end probabilistic model for geocoding text data.
Additionally, we collect a novel data set for evaluating the performance of
geocoding systems. We compare the model-based solution, called ELECTRo-map, to
the current state-of-the-art open source system for geocoding texts for event
data. Finally, we discuss the benefits of end-to-end model-based geocoding,
including principled uncertainty estimation and the ability of these models to
leverage contextual information.

提出了一种基于概率模型的文本数据地理编码方法 (ELECTRo-map)，用于推断或提取描述行为、日期、时间和位置等信息。本文还对比了该方法与当前开源系统的性能，探讨了端到端模型的优势，包括原则性不确定性估计和利用上下文信息的能力。

基于文本信息的概率地理编码位置回归

Regressing Location on Text for Probabilistic Geocoding

More than half of the world's roads lack adequate street addressing systems.
Lack of addresses is even more visible in daily lives of people in developing
countries. We would like to object to the assumption that having an address is
a luxury, by proposing a generative address design that maps the world in
accordance with streets. The addressing scheme is designed considering several
traditional street addressing methodologies employed in the urban development
scenarios around the world. Our algorithm applies deep learning to extract
roads from satellite images, converts the road pixel confidences into a road
network, partitions the road network to find neighborhoods, and labels the
regions, roads, and address units using graph- and proximity-based algorithms.
We present our results on a sample US city, and several developing cities,
compare travel times of users using current ad hoc and new complete addresses,
and contrast our addressing solution to current industrial and open geocoding
alternatives.

本研究提出了一种基于深度学习和图像区分的地址生成方案，该方案可以通过从卫星图像中提取道路信息、划分社区、标记区域与地址单位等步骤，映射全球道路，并与当前工业和开源地址编码方案进行了比较。