Automatic Construction of Large-Scale Geoparsing Corpus Using Wikipedia Hyperlinks

Core Concepts

Geoparsing corpus construction using Wikipedia hyperlinks for large-scale data.

Abstract

Geoparsing involves estimating coordinates of location expressions in texts. Previous corpora for geoparsing were limited in scale and domain coverage. WHLL method leverages Wikipedia hyperlinks to annotate location expressions with coordinates. The WHLL corpus consists of 1.3M articles with 7.8 unique location expressions each. Disambiguating location expressions remains a challenge for geoparsing systems.

Stats

The WHLL corpus consists of 1.3M articles, each containing about 7.8 unique location expressions. 45.6% of location expressions are ambiguous and refer to more than one location with the same notation.

Quotes

"We propose Wikipedia Hyperlink-based Location Linking (WHLL), a novel method to construct a large-scale corpus for geoparsing from Wikipedia articles." "With this method, we constructed the WHLL corpus, a new large-scale corpus for geoparsing."

Key Insights Distilled From

Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks

by Keyaki Ohno,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16483.pdf

Deeper Inquiries

How can leveraging hyperlinks in Wikipedia improve the accuracy of assigning coordinates to location expressions?

By leveraging hyperlinks in Wikipedia, the method proposed in the context can improve the accuracy of assigning coordinates to location expressions in several ways: Rich Information Source: Hyperlinks provide additional context and information about location expressions by linking them to other articles with relevant details. This extra information helps in accurately determining the correct coordinates for ambiguous or multi-referential locations. Cross-Referencing: Hyperlinks allow for cross-referencing between different articles, enabling a more comprehensive understanding of how various locations are related geospatially. This interconnectedness aids in disambiguating similar-sounding or similarly named locations. Consistency and Reliability: Since Wikipedia is a widely used and reliable source of information, leveraging its hyperlinks ensures consistent annotations across multiple articles. The credibility of Wikipedia as a primary source enhances the reliability of assigned coordinates.

What are the implications of having a large-scale geoparsing corpus like WHLL on machine learning models?

Having a large-scale geoparsing corpus like WHLL has significant implications on machine learning models: Improved Model Performance: Machine learning models trained on larger datasets tend to perform better due to exposure to diverse examples and scenarios present in real-world data. Enhanced Generalization: With a vast amount of annotated data from diverse geographical locations, machine learning models trained on WHLL can generalize better when faced with new or unseen location expressions during inference. Robustness Against Ambiguity: Large-scale corpora like WHLL containing ambiguous location expressions help train models that are robust against ambiguity challenges commonly encountered in geoparsing tasks. Scalability and Adaptability: Models trained on extensive datasets like WHLL have greater scalability potential and adaptability to varying domains or applications within geoparsing.

How can the challenges faced in disambiguating location expressions be addressed effectively?

To address challenges faced in disambiguating location expressions effectively, several strategies can be employed: Contextual Clues: Utilize contextual information surrounding ambiguous terms within text snippets to infer their intended meanings based on neighboring words or phrases. Dependency Parsing: Employ dependency parsing techniques as seen in the context's Dependency-based strategy where relationships between tokens guide coordinate assignment decisions for related entities. Familiarity-based Approaches: Consider familiarity-based strategies where frequently mentioned or well-known locations are prioritized during coordinate assignment if direct matches exist within geographic databases like GeoNames. 4Machine Learning Models: Train machine learning models specifically designed for resolving ambiguities by incorporating features such as word embeddings, attention mechanisms, or entity co-occurrence patterns from large corpora like WHLL. These approaches collectively contribute towards enhancing disambiguation accuracy while dealing with complex cases involving multiple possible interpretations for given location references."