Using Recurrent Neural Networks for Toponym Resolution in Text

Abstract

Toponym resolution refers to the disambiguation of place names and other references to places present in textual documents, resolving them to unambiguous geographical identifiers (e.g., geographic coordinates of latitude and longitude). One of the major challenges in this task is that, usually, place names are highly ambiguous (e.g., there are several locations on the surface of the Earth that share the same name). In this paper, we propose to address the task through a recurrent neural network architecture with multiple inputs and outputs, specifically leveraging pre-trained contextual embeddings (ELMo) and bi-directional Long Short-Term Memory (LSTM) units, both commonly used for textual data modeling. The proposed model was tested on two datasets that were previously used to evaluate toponym resolution systems, namely the War of the Rebellion and the Local-Global Lexicon corpora. The obtained results outperform state-of-the-art results, confirming the superiority of the proposed method over other previous approaches.