{"url":"/task/cell-entity-annotation","name":"Cell Entity Annotation","slug":"cell-entity-annotation","description_markdown":"**Cell Entity Annotation** (CEA) is the task of annotating cells in a table with an entity from a knowledge base and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). CEA problem labels are entities from knowledge bases such as DBpedia or WikiData. It usually is considered as a multi-class classification problem.\r\n\r\nCEA can also be referred to in different works as the problem of entity linking, as it links a cell in a table to an entity.","categories":[{"name":"Knowledge Base","url":"/area/knowledge-base"},{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":13,"papers_with_code":6,"benchmarks":5,"benchmark_tables_in_archive":5,"benchmark_tables_shown":5,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":4,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/cell-entity-annotation-on-biodivtab","slug":"cell-entity-annotation-on-biodivtab","dataset":"BiodivTab","dataset_url":"/dataset/biodivtab","rows_in_archive":5,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"KGCODE-Tab","paper_title":"KGCODE-Tab Results for SemTab 2022","paper_url":"/paper/kgcode-tab-results-for-semtab-2022","paper_date":"2022-10-25","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/cell-entity-annotation-on-toughtables-dbp","slug":"cell-entity-annotation-on-toughtables-dbp","dataset":"ToughTables-DBP","dataset_url":"/dataset/tough-tables","rows_in_archive":5,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"DAGOBAH","paper_title":"DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data","paper_url":"/paper/dagobah-table-and-graph-contexts-for","paper_date":"2021-10-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/cell-entity-annotation-on-toughtables-wd","slug":"cell-entity-annotation-on-toughtables-wd","dataset":"ToughTables-WD","dataset_url":"/dataset/tough-tables","rows_in_archive":5,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"DAGOBAH","paper_title":"From Heuristics to Language Models: A Journey Through the Universe of Semantic Table Interpretation with DAGOBAH","paper_url":"/paper/from-heuristics-to-language-models-a-journey","paper_date":"2022-10-25","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/cell-entity-annotation-on-wikipediags","slug":"cell-entity-annotation-on-wikipediags","dataset":"WikipediaGS","dataset_url":"/dataset/wikipediags","rows_in_archive":1,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"TURL","paper_title":"TURL: Table Understanding through Representation Learning","paper_url":"/paper/turl-table-understanding-through","paper_date":"2020-06-26","arxiv_id":"2006.14806","code_links":[{"title":"sunlab-osu/TURL","url":"https://github.com/sunlab-osu/TURL"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/cell-entity-annotation-on-wikitables-turl-cea","slug":"cell-entity-annotation-on-wikitables-turl-cea","dataset":"WikiTables-TURL-CEA","dataset_url":"/dataset/wikitables-turl","rows_in_archive":1,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"TURL","paper_title":"TURL: Table Understanding through Representation Learning","paper_url":"/paper/turl-table-understanding-through","paper_date":"2020-06-26","arxiv_id":"2006.14806","code_links":[{"title":"sunlab-osu/TURL","url":"https://github.com/sunlab-osu/TURL"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/tough-tables","name":"Tough Tables","full_name":"","num_papers_in_archive":11},{"url":"/dataset/biodivtab","name":"BiodivTab","full_name":"","num_papers_in_archive":8},{"url":"/dataset/wikitables-turl","name":"WikiTables-TURL","full_name":"","num_papers_in_archive":7},{"url":"/dataset/wikipediags","name":"WikipediaGS","full_name":"","num_papers_in_archive":4}],"subtasks":[],"parent_tasks":[{"url":"/task/table-annotation","name":"Table annotation"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":6,"of":6,"tagged_in_all":13,"items":[{"url":"/paper/foundation-models-meet-imbalanced-single-cell","title":"Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations","date":"2023-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-an-approach-based-on-knowledge-graph","title":"Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching","date":"2022-10-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/magic-mining-an-augmented-graph-using-ink","title":"MAGIC: Mining an Augmented Graph using INK, starting from a CSV","date":"2021-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/jentab-meets-semtab-2021-s-new-challenges","title":"JenTab Meets SemTab 2021's New Challenges","date":"2021-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tough-tables-carefully-evaluating-entity","title":"Tough Tables: Carefully Evaluating Entity Linking for Tabular Data","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/turl-table-understanding-through","title":"TURL: Table Understanding through Representation Learning","date":"2020-06-26","arxiv_id":"2006.14806","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":1,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}