Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses

David Beauchemin, Marouane Yassine

Abstract
Segmenting an address into meaningful components, also known as address parsing, is an essential step in many applications from record linkage to geocoding and package delivery. Consequently, a lot of work has been dedicated to develop accurate address parsing techniques, with machine learning and neural network methods leading the state-of-the-art scoreboard. However, most of the work on address parsing has been confined to academic endeavours with little availability of free and easy-to-use open-source solutions.This paper presents Deepparse, a Python open-source, extendable, fine-tunable address parsing solution under LGPL-3.0 licence to parse multinational addresses using state-of-the-art deep learning algorithms and evaluated on over 60 countries. It can parse addresses written in any language and use any address standard. The pre-trained model achieves average 99% parsing accuracies on the countries used for training with no pre-processing nor post-processing needed. Moreover, the library supports fine-tuning with new data to generate a custom address parser.
Anthology ID:
2023.nlposs-1.3
Volume:
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
Month:
December
Year:
2023
Address:
Singapore
Editors:
Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
Venues:
NLPOSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–24
Language:
URL:
https://aclanthology.org/2023.nlposs-1.3
DOI:
10.18653/v1/2023.nlposs-1.3
Bibkey:
Cite (ACL):
David Beauchemin and Marouane Yassine. 2023. Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 19–24, Singapore. Association for Computational Linguistics.
Cite (Informal):
Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses (Beauchemin & Yassine, NLPOSS-WS 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2023.nlposs-1.3.pdf
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