Cross-Lingual Learning Strategies for Improving Product Matching Quality-Reference-Cited by-同舟云学术

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Cross-Lingual Learning Strategies for Improving Product Matching Quality

Published:2024-04-08 Issue: Volume:1 Page:313-320
ISSN:
Container-title:Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
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Author:

Alves Andre Luiz Firmino¹^ORCID,Baptista Claudio de Souza²^ORCID,Barbosa Luciano³^ORCID,Araujo Clecio B. M.⁴^ORCID

Affiliation:

1. Federal Institute of Paraíba, Campina Grande, PB, Brazil

2. Information Systems Laboratory (LSI), Federal University of Campina Grande, Campina Grande, Paraíba, Brazil

3. Federal University of Pernambuco, Recife, Pernambuco, Brazil

4. Federal Institute of Paraíba, Picuí, Paraíba, Brazil

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605098.3636001

Reference35 articles.

1. A self-verifying clustering approach to unsupervised matching of product titles

2. Learning representations of Web entities for entity resolution

3. Nils Barlaug and Jon Atle Gulla. 2021. Neural networks for entity matching: A survey. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 3 (2021), 1--37.

4. On evaluating text similarity measures for customer data deduplication

5. Peter Christen. 2012. Data Matching Systems. In Data Matching. Springer Berlin Heidelberg, 229--242.

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