Documents hold spatial focus and valuable locality characteristics. For
example, descriptions of listings in real estate or travel blogs contain
information about specific local neighborhoods. This information is valuable to
characterize how humans perceive their environment. However, the first step to
making use of this information is to identify the spatial focus (e.g., a city)
of a document. Traditional approaches for identifying the spatial focus of a
document rely on detecting and disambiguating toponyms from the document. This
approach requires a vocabulary set of location phrases and ad-hoc rules, which
ignore important words related to location. Recent topic modeling approaches
using large language models often consider a few topics, each with broad
coverage. In contrast, the spatial focus of a document can be a country, a
city, or even a neighborhood, which together, is much larger than the number of
topics considered in these approaches. Additionally, topic modeling methods are
often applied to broad topics of news articles where context is easily
distinguishable. To identify the geographic focus of a document effectively, we
present a simple but effective Joint Embedding of multi-LocaLitY (JELLY), which
jointly learns representations with separate encoders of document and location.
JELLY significantly outperforms state-of-the-art methods for identifying
spatial focus from documents from a number of sources. We also demonstrate case
studies on the arithmetic of the learned representations, including identifying
cities with similar locality characteristics and zero-shot learning to identify
document spatial focus.

文档的定位特点和重要的本地特征。因此，我们提出了一种名为 JELLY 的简单而有效的联合嵌入方法，它在多位置（multi-LocaLitY）下同时学习文档和位置的编码器，能够更有效地识别文档空间重点。