{"url":"/sota/entity-resolution-on-wdc-computers-small","task":{"name":"Entity Resolution","url":"/task/entity-resolution","note":null},"dataset":{"name":"WDC Computers-small","url":"/dataset/wdc-products"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Entity resolution** (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)\r\n\r\nSurveys on entity resolution:\r\n\r\n- [Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey](https://arxiv.org/pdf/1905.06397.pdf), 2020.\r\n\r\n- [Barlaug and Gulla: Neural Networks for Entity Matching: A Survey](https://arxiv.org/pdf/2010.11075.pdf), 2021.\r\n\r\nThe task of entity resolution is closely related to the task of [entity alignment](https://paperswithcode.com/task/entity-alignment) which focuses on matching entities between knowledge bases. The task of [entity linking](https://paperswithcode.com/task/entity-linking) differs from entity resolution as entity linking focuses on identifying entity mentions in free text.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1 (%)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 (%)":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"BERT","metrics":{"F1 (%)":"96.53"},"uses_additional_data":true,"paper_date":"2020-08-31","paper":"/paper/intermediate-training-of-bert-for-product","paper_url":"https://madoc.bib.uni-mannheim.de/57403/","paper_title":"Intermediate Training of BERT for Product Matching","code":"https://github.com/weyoun2211/productbert-intermediate","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"RoBERTa-SupCon","metrics":{"F1 (%)":"95.21"},"uses_additional_data":false,"paper_date":"2022-02-04","paper":"/paper/supervised-contrastive-learning-for-product","paper_url":"https://arxiv.org/abs/2202.02098v2","paper_title":"Supervised Contrastive Learning for Product Matching","code":"https://github.com/wbsg-uni-mannheim/contrastive-product-matching","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"HG","metrics":{"F1 (%)":"88.50"},"uses_additional_data":false,"paper_date":"2022-06-01","paper":"/paper/entity-resolution-with-hierarchical-graph","paper_url":"https://dl.acm.org/doi/10.1145/3514221.3517872","paper_title":"Entity Resolution with Hierarchical Graph Attention Networks","code":"https://github.com/CGCL-codes/HierGAT","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"DADER-MMD","metrics":{"F1 (%)":"88.00"},"uses_additional_data":false,"paper_date":"2022-06-01","paper":"/paper/domain-adaptation-for-deep-entity-resolution","paper_url":"https://dl.acm.org/doi/10.1145/3514221.3517870","paper_title":"Domain Adaptation for Deep Entity Resolution: A Design Space Exploration","code":"https://github.com/ruc-datalab/DADER","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"Ditto","metrics":{"F1 (%)":"80.76"},"uses_additional_data":false,"paper_date":"2020-04-01","paper":"/paper/deep-entity-matching-with-pre-trained","paper_url":"https://arxiv.org/abs/2004.00584v3","paper_title":"Deep Entity Matching with Pre-Trained Language Models","code":"https://github.com/megagonlabs/ditto","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"JointBERT","metrics":{"F1 (%)":"77.55"},"uses_additional_data":false,"paper_date":"2021-06-01","paper":"/paper/dual-objective-fine-tuning-of-bert-for-entity","paper_url":"https://doi.org/10.14778/3467861.3467878","paper_title":"Dual-Objective Fine-Tuning of BERT for Entity Matching","code":"https://github.com/wbsg-uni-mannheim/jointbert","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}