  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.
