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2020
Conference paper (unpublished)
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
Recognizing toponyms and resolving them to their real-world referents is required for providing 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 toponym... -
2020
Conference paper (published)
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking. Its pair classifier supports various deep neural network architectures for training new classifiers and for fine-tuning a pretrained model, which paves the way for transfer learning in fuzzy string matching. This approach...Hosseini, Kasra ; Nanni, Federico ; Coll Ardanuy, Mariona
Natural Language Processing, string matching, toponym matching, machine learning, and digital humanities