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We first define a large space of DL solutions for blocking, which contains solutions of varying complexity and subsumes most previous works. Next, we develop eight representative solutions in this space. These solutions do not require labeled training data and exploit recent advances in DL (e.g., sequence modeling, transformer, self supervision). We empirically determine which solutions perform best on what kind of datasets (structured, textual, or dirty). We show that the best solutions (among the above eight) outperform the best existing DL solution and the best existing non-DL solutions (including a state-of-the-art industrial non-DL solution), on dirty and textual data, and are comparable on structured data. Finally, we show that the combination of the best DL and non-DL solutions can perform even better, suggesting a new venue for research.<\/jats:p>","DOI":"10.14778\/3476249.3476294","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T16:46:23Z","timestamp":1635353183000},"page":"2459-2472","source":"Crossref","is-referenced-by-count":76,"title":["Deep learning for blocking in entity matching"],"prefix":"10.14778","volume":"14","author":[{"given":"Saravanan","family":"Thirumuruganathan","sequence":"first","affiliation":[{"name":"HBKU, Qatar"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"Amazon"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"HBKU, Qatar"}]},{"given":"Mourad","family":"Ouzzani","sequence":"additional","affiliation":[{"name":"HBKU, Qatar"}]},{"given":"Yash","family":"Govind","sequence":"additional","affiliation":[{"name":"Informatica"}]},{"given":"Derek","family":"Paulsen","sequence":"additional","affiliation":[{"name":"UW-Madison"}]},{"given":"Glenn","family":"Fung","sequence":"additional","affiliation":[{"name":"American Family Insurance"}]},{"given":"AnHai","family":"Doan","sequence":"additional","affiliation":[{"name":"UW-Madison"}]}],"member":"320","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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