DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
Name
2020.emnlp-demos.9.pdf
Description
visibility:open
Size
789.39 KB
Format
Adobe PDF
Checksum (CRC64NVME)
9BEY+KW2cAM=
Resource type
Conference paper (published)
Date published
October 2020
Abstract
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 is especially useful where only limited training examples are available. The learned DeezyMatch models can be used to generate rich vector representations from string inputs. The candidate ranker component in DeezyMatch uses these vector representations to find, for a given query, the best matching candidates in a knowledge base. It uses an adaptive searching algorithm applicable to large knowledge bases and query sets. We describe DeezyMatch’s functionality, design and implementation, accompanied by a use case in toponym matching and candidate ranking in realistic noisy datasets.
Contributor (organisation)
British Library
Living with Machines
Project(s)
Living with Machines
Editor
Liu, Qun
Schlangen, David
Funder
| Funder name | Awards |
Engineering and Physical Sciences Research Council | EP/N510129/1 |
Alan Turing Institute | EP/N510129/1 |
Arts and Humanities Research Council | AH/S01179X/1 |
Event title
2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Publisher
Association for Computational Linguistics
Official URL
Collection