A Coherent Unsupervised Model for Toponym Resolution

TL;DR

This paper introduces an unsupervised model for toponym resolution that effectively disambiguates geographic mentions in text without requiring training data, outperforming some commercial tools and approaching supervised methods.

Contribution

The paper proposes a novel unsupervised approach that leverages document geographic scope and inter-toponym relationships to resolve locations without training data.

Findings

Outperforms unsupervised techniques and some commercial tools on multiple datasets.

Close performance to supervised methods, especially with many unseen toponyms.

Effective in scenarios lacking annotated training data.

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.