A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
TL
TL;DR
This paper introduces a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures and evaluates its performance in the context of geographical candidate selection in English and Spanish.
Abstract
Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously recognized toponym. While it has traditionally received little attention, candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors) and assess its performance in the context of geographical candidate selection in English and Spanish.
References38 items
1
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
2
Resolving places, past and present: toponym resolution in historical british newspapers using multiple resources
3
Optimal Transport-based Alignment of Learned Character Representations for String Similarity
4
Joint Learning of Named Entity Recognition and Entity Linking
5