Dealing with Acronyms, Abbreviations, and Typos in Real-World Entity Matching

Summary: Smash: a similarity measure with a dynamic-programming algorithm that jointly handles acronyms, abbreviations, and typos for entity/record matching without needing pre-specified synonym rules. Two optimizations and OpenRefine integration yield large F‑score gains over strong baselines (including GPT‑4). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13798
Venue: VLDB
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,293 | 22.52%
DOI: 10.14778/3685800.3685830

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Joshua Wu (University of California Berkeley)
2. Dixin Tang (University of Texas)
3. Nithin Chalapathi (University of California Berkeley)
4. Tristan Chambers (University of California Berkeley)
5. Julie Ciccolini (Techtivist)
6. Cheryl Phillips (Stanford University)
7. Lisa Pickoff-White (University of California Berkeley)
8. Aditya Parameswaran (University of California Berkeley)

BibTeX Citation

@article{wu_vldb24,
        title = {{Dealing with Acronyms, Abbreviations, and Typos in Real-World Entity Matching}},
        author = {Wu, Joshua and Tang, Dixin and Chalapathi, Nithin and Chambers, Tristan and Ciccolini, Julie and Phillips, Cheryl and Pickoff-White, Lisa and Parameswaran, Aditya},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {12},
        pages = {4104--4116},
        doi = {10.14778/3685800.3685830},
        url = {https://doi.org/10.14778/3685800.3685830},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
141	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.0002964847
169	Efficient Exact Set-Similarity Joins	2006	VLDB	0.0002743469
911	Ed-Join: An Efficient Algorithm for Similarity Joins With Edit Distance Constraints	2008	VLDB	0.00013283031
975	Can We Beat the Prefix Filtering? An Adaptive Framework for Similarity Join and Search	2012	SIGMOD	0.00012870645
1,886	Pass-Join: A Partition-based Method for Similarity Joins	2012	VLDB	9.5358137e-05
2,186	String Similarity Joins: An Experimental Evaluation	2014	VLDB	9.0001436e-05
2,501	An Empirical Evaluation of Set Similarity Join Techniques	2016	VLDB	8.4975661e-05
3,547	Learning Semantic String Transformations from Examples	2012	VLDB	7.3225973e-05
3,731	Trie-Join: Efficient Trie-based String Similarity Joins with Edit-Distance Constraints	2010	VLDB	7.1663952e-05
4,023	Smurf: Self-Service String Matching Using Random Forests	2019	VLDB	6.949387e-05
4,396	Approximate String Joins with Abbreviations	2018	VLDB	6.7268636e-05
4,525	SEMA-JOIN: Joining Semantically-Related Tables Using Big Table Corpora	2015	VLDB	6.6456999e-05
4,707	SILKMOTH: An Efficient Method for Finding Related Sets with Maximum Matching Constraints	2017	VLDB	6.552423e-05
5,088	String Similarity Measures and Joins with Synonyms	2013	SIGMOD	6.3673276e-05
6,115	Fine-grained Concept Linking using Neural Networks in Healthcare	2018	SIGMOD	5.9709282e-05
9,702	Towards a Unified Framework for String Similarity Joins	2019	VLDB	5.2351259e-05

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2	141	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB
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4	7,714	Incorporating String Transformations in Record Matching	2008	SIGMOD
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