Multi-Level Gazetteer-Free Geocoding (2021)

Abstract: We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth’s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates—and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.

## introduction geocoding is the task of resolving location references in text to geographic coordinates or regions. it is often studied in social networks, where metadata and the network itself provide additional non-textual signals @xcite . if locations can be mapped to an entity in a knowledge graph, toponym resolution -a special case of entity resolution -can be used to resolve references to locations. past work used heuristics based on location popularity @xcite and distance between candidate locations @xcite , as well as learned associations from text to locations. however, such approaches have a strong bias for highly-populated locations, especially for social media. we present multi-level geocoder (mlg, fig. 1 ), a model that learns spatial language representations and maps toponyms to coordinates on earth's surface. this geocoder is not restricted to resolving toponyms to specific location entities, but rather to geo-coordinates directly. mlg can thus be extended to any arbitrary location references in future without having to rely on its presence in the gazetteer. for comparative evaluation, we use three english toponym resolution datasets from ## spatial representations geocoders map text spans to geo-coordinates-a prediction over a continuous space representing the surface of a sphere. we relax the problem from continuous space to discrete space by quantizing the earth's surface as a grid and performing multi-class prediction over the grid's cells. we construct a hierarchical grid using the s2 library. 3 s2 projects the six faces of a cube onto the earth's surface and each face is recursively divided into 4 quadrants, as shown in figure 1 . cells at each level are indexed using a hilbert curve. each s2 cell is represented as a 64-bit unsigned integer and can correspond to areas as small as ≈1cm 2 . s2 cells preserve cell size across the globe better than commonly-used degreesquare grids (e.g. 1 our experiments go as far as s2 level eight (of thirty), but our approach is extendable to any level of granularity and could support very fine-grained locations like buildings and landmarks. the built-in hierarchical nature of s2 cells makes it well suited as a scaffold for models that learn and combine evidence from multiple levels. this combines the best of both worlds: specificity at finer levels and aggregation/smoothing at coarser levels. roller et al. (2012) use adaptive, variable shaped cells based on k-d trees; such grids can adapt to the different 2 https://github.com/ google-research-datasets/mlg_evaldata 3 https://s2geometry.io/ s2 level number of cells avg area l4 1.5k 332 l5 6.0k 83 l6 24.0k 21 l7 98.0k 5 l8 393.0k 1 in accordance with the nature of the problem over continuous space, studies using bivariate gaussians on multiple flattened regions @xcite ) perform well on distance based metrics, but this involves difficult trade-offs between flattened region sizes and the level of distortion they introduce. some of the early models used with grid-based representations were probabilistic language models that produce document likelihoods in different geospatial cells @xcite @xcite . extensions include domain adapting language models from various sources @xcite , hierarchical discriminative models @xcite , and smoothing sparse grids with gaussian priors @xcite . alternatively, fornaciari and hovy (2019a) use a multi-task learning setup that assigns probabilities across grids and also predicts the true location through regression. @xcite cover a broad survey of document geocoding. much of this work has been conducted on social media data like twitter, where additional information beyond the text-such as the network connections and user and document metadata-have been used @xcite @xcite @xcite . mlg is not trained on social media data and hence, does not need additional network information. further, the data does not have a character limit like tweets, so models can learn from long text sequences. ## multi-level geocoder (mlg) multi-level geocoder (mlg, figure 2 ) is a text-tolocation cnn-based geocoder. context features are similar to camcoder @xcite but we exclude its metadata-based mapvec feature. locations are represented using a hierarchical s2 grid; this enables joint multi-level prediction, by optimizing for total loss computed from all levels.