Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window

Abstract

Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words.

Semantic similarity measure of natural language text through machine learning and a keyword‐aware cross‐encoder‐ranking summarizer—A case study using UCGIS GIS&T body of knowledge

“…These new models are capable of learning universal language representations, which are beneficial for a variety of downstream NLP tasks such as semantic similarity measurement (Qiu et al, 2020). Semantic similarity measures the degree to which two textual pieces carry similar meaning and this measure has been widely used in the GIS domain to help find related geographic terms (Ballatore et al, 2013), match spatial entity descriptions (Ma et al, 2018), identify similar spatial features (Li et al, 2012), leverage spatial context semantics (Wang, Fei, et al, 2020), and so on.…”