Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching
Abstract
Automatic co-text free name matching has a variety of important real-world applications, ranging from fiscal compliance to border control. Name matching systems use a variety of engines to compare two names for similarity, with one of the most critical being phonetic name similarity. In this work, we re-frame existing work on neural sequence-to-sequence transliteration such that it can be applied to name matching. Subsequently, for performance reasons, we then build upon this work to utilize an alternative, non-recurrent neural encoder module. This ultimately yields a model which is 63% faster while still maintaining a 16% improvement in averaged precision over our baseline model.
Cite
Text
Blair et al. "Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86517-7_17Markdown
[Blair et al. "Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/blair2021ecmlpkdd-balancing/) doi:10.1007/978-3-030-86517-7_17BibTeX
@inproceedings{blair2021ecmlpkdd-balancing,
title = {{Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching}},
author = {Blair, Philip and Eliav, Carmel and Hasanaj, Fiona and Bar, Kfir},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2021},
pages = {271-284},
doi = {10.1007/978-3-030-86517-7_17},
url = {https://mlanthology.org/ecmlpkdd/2021/blair2021ecmlpkdd-balancing/}
}