A Coherent Unsupervised Model for Toponym Resolution

Abstract

Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content. However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task. In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data. We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing. Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution. Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques. We also examine three commercial products including Reuters OpenCalais, Yahoo! YQL Placemaker, and Google Cloud Natural Language API. The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.

A pragmatic guide to geoparsing evaluation

“…A true positive was judged as a correctly geotagged toponym and one resolved to within a certain distance. This ranges from 5 km (Andogah 2010; Lieberman and Samet 2012) to 10 miles (Kamalloo and Rafiei 2018;Lieberman et al 2010) to all of the previous thresholds (Kolkman 2015) including 100 km and 161 km. In cases where WordNet has been used as the ground truth (Buscaldi et al 2010) an F-Score might be appropriate given WordNet's structure but it is not possible to make a comparison with a coordinatesbased geoparser.…”

SemEval-2019 Task 12: Toponym Resolution in Scientific Papers

“…Such metadata can be geotagging of social media posts (Zhang and Gelernter, 2014) or external databases structuring the information detailed in a document (Weissenbacher et al, 2015). These three strategies are complementary and can be unified with machine learning algorithms as shown by (Santos et al, 2015) or (Kamalloo and Rafiei, 2018).…”

Multi-Level Gazetteer-Free Geocoding

“…This combines the best of both worlds: specificity at finer levels and aggregation/smoothing at coarser levels. Roller et al (2012) (Kamalloo and Rafiei, 2018). Polygons for geopolitical entities such as city, state, and country are perhaps ideal, but these too require detailed metadata for all toponyms, managing non-uniformity of the polygons, and general facility with GIS tools.…”