Earth:Toponym Resolution

In Geographic Information Systems, toponym resolution is the process of mapping between a toponym, i.e. the mention of a place, and an unambiguous spatial footprint of the same place. The same geographic names have historically been used by emigrant settlers to denote their new homes, leading to referential ambiguity of place names. Sometimes, the original name gets modified (as in "York" vs. "New York"). In many cases, a name is reused without modification ("Boston" in England, UK vs. "Boston" in Massachusetts, USA). To map a set of place names or toponyms that occur in a document to their corresponding latitude/longitude coordinates, a polygon, or any other spatial footprint, a disambiguation step is necessary. A toponym resolution algorithm is an automatic method that performs a mapping from a toponym to a spatial footprint.

Most methods for toponym resolution employ a gazetteer of possible mappings between names and spatial footprints.

In contrast to geocoding of postal addresses, which are typically stored in structured database records, toponym resolution is typically applied to large unstructured text document collections to associate the locations mentioned in them with maps.

Resolution Process

The process of annotating media (e.g., image, text, video) using spatial footprints is known as Geotagging. In order to automatically geotag a text document, the following steps are usually undertaken: toponym recognition (i.e., spotting textual references to geographic locations) and toponym resolution (i.e., selecting an appropriate location interpretation for each geographic reference).

Toponym Recognition can be considered as a special case of Named-entity recognition where the objective is to merely derive location entities. However, the result of Named-entity recognition can be further improved using hand-crafted rules or statistical rules.

For obtaining location interpretations, resolution models tend to leverage gazetteers (i.e., huge databases of locations) such as GeoNames and OpenStreetMap. A naive approach to resolve toponyms is to pick the most populated interpretation from the list of candidates. For example, in the following excerpt:

Toronto man living, working in London 'uncertain of future' in U.K. after Brexit

The naive approach seems viable since toponyms Toronto and London refer to their most common interpretation, located in Canada and Britain respectively, whereas in the following piece from a news article:

High-speed rail between Toronto and London by 2025

—CBC

This approach fails to pinpoint toponym London as the city located in Ontario, Canada. Hence, selecting the highest population cannot work well for toponyms in a localized context.

Additionally, toponym resolution does not address metonymy in general. Nonetheless, a resolution technique can still disambiguate a metonymy reference as long as it is identified as a toponym in the recognition phase. For instance, in the following excerpt:

Canada is also adjusting its driving laws to account for cannabis DUIs.

—Esquire

Canada indicates a metonymy and refers to "the government of Canada". However, it can be identified as a location by a generic Named-entity recognizer and thus, a toponym resolver is able to disambiguate it.

Approaches

Toponym resolution methods can be generally divided into supervised and unsupervised models. Supervised methods typically cast the problem as a learning task wherein the model first extracts contextual and non-contextual features and then, a classifier is trained on a labelled dataset. Adaptive model is one of the prominent models proposed in resolving toponyms. For each interpretation of a toponym, the model derives context-sensitive features based on geographical proximity and sibling relationships with other interpretations. In addition to context related features, the model benefits from context-free features including population, and audience location. On the other hand, unsupervised models do not warrant annotated data. They are superior to supervised models when the annotated corpus is not sufficiently large, and supervised models may not generalize well.

Unsupervised models tend to better exploit the interplay of toponyms mentioned in a document. The Context-Hierarchy Fusion model estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. By means of mapping the problem to a conflict-free set cover problem, this model achieves a coherent and robust resolution.

Furthermore, adopting Wikipedia and knowledge bases have been shown effective in toponym resolution. TopoCluster models the geographical senses of words by incorporating Wikipedia pages of locations and disambiguates toponyms using the spatial senses of the words in the text.

See also

• Geoparsing
• Geographic Information Systems
• Information extraction
• Natural Language Processing