{"url":"/dataset/ace-2004","name":"ACE 2004","full_name":"ACE 2004 Multilingual Training Corpus","description_markdown":"**ACE 2004** Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2004 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities and relations and was created by Linguistic Data Consortium with support from the ACE Program, with additional assistance from the DARPA TIDES (Translingual Information Detection, Extraction and Summarization) Program.\r\nThe objective of the ACE program is to develop automatic content extraction technology to support automatic processing of human language in text form. In September 2004, sites were evaluated on system performance in six areas: Entity Detection and Recognition (EDR), Entity Mention Detection (EMD), EDR Co-reference, Relation Detection and Recognition (RDR), Relation Mention Detection (RMD), and RDR given reference entities. All tasks were evaluated in three languages: English, Chinese and Arabic.\r\n\r\nSource: [https://catalog.ldc.upenn.edu/LDC2005T09](https://catalog.ldc.upenn.edu/LDC2005T09)","description_withheld":null,"homepage":"https://catalog.ldc.upenn.edu/LDC2005T09","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Ace 2004 multilingual training corpus","first_author":null,"url":"https://catalog.ldc.upenn.edu/LDC2005T09"},"license":{"name":"Custom","url":"https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"},{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"UIE","url":"/task/uie","datasets_with_task":"/datasets/task/uie"},{"name":"Entity Disambiguation","url":"/task/entity-disambiguation","datasets_with_task":"/datasets/task/entity-disambiguation"},{"name":"Nested Named Entity Recognition","url":"/task/nested-named-entity-recognition","datasets_with_task":"/datasets/task/nested-named-entity-recognition"},{"name":"Nested Mention Recognition","url":"/task/nested-mention-recognition","datasets_with_task":"/datasets/task/nested-mention-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Mandarin Chinese","url":"/datasets/language/mandarin-chinese"},{"name":"Standard Arabic","url":"/datasets/language/standard-arabic"}],"variants":["ACE 2004","ACE2004"],"data_loaders":[],"num_papers_in_archive":51,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/nested-named-entity-recognition-on-ace-2004","task":"Nested Named Entity Recognition","dataset_variant":"ACE 2004","rows":24,"metrics":["F1"],"first_row_in_archive_order":{"model":"PromptNER [RoBERTa-large]","paper":"/paper/promptner-prompt-locating-and-typing-for","metrics":{"F1":"88.72"},"code_links":[{"title":"tricktreat/promptner","url":"https://github.com/tricktreat/promptner"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-ace-2004","task":"Relation Extraction","dataset_variant":"ACE 2004","rows":11,"metrics":["RE+ Micro F1","RE Micro F1","NER Micro F1","Cross Sentence"],"first_row_in_archive_order":{"model":"PL-Marker","paper":"/paper/pack-together-entity-and-relation-extraction","metrics":{"Cross Sentence":"Yes","NER Micro F1":"90.4","RE Micro F1":"69.7","RE+ Micro F1":"66.5"},"code_links":[{"title":"tomaarsen/spanmarkerner","url":"https://github.com/tomaarsen/spanmarkerner"},{"title":"thunlp/pl-marker","url":"https://github.com/thunlp/pl-marker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/named-entity-recognition-on-ace-2004","task":"Named Entity Recognition (NER)","dataset_variant":"ACE 2004","rows":9,"metrics":["F1","Multi-Task Supervision"],"first_row_in_archive_order":{"model":"Ours: cross-sentence ALB","paper":"/paper/a-frustratingly-easy-approach-for-joint","metrics":{"F1":"90.3","Multi-Task Supervision":"y"},"code_links":[{"title":"princeton-nlp/PURE","url":"https://github.com/princeton-nlp/PURE"},{"title":"YaoXinZhi/BERT-for-BioNLP-OST2019-AGAC-Task2","url":"https://github.com/YaoXinZhi/BERT-for-BioNLP-OST2019-AGAC-Task2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/nested-mention-recognition-on-ace-2004","task":"Nested Mention Recognition","dataset_variant":"ACE 2004","rows":7,"metrics":["F1"],"first_row_in_archive_order":{"model":"BoningKnife","paper":"/paper/boningknife-joint-entity-mention-detection","metrics":{"F1":"86.41"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/entity-disambiguation-on-ace2004","task":"Entity Disambiguation","dataset_variant":"ACE2004","rows":6,"metrics":["Micro-F1"],"first_row_in_archive_order":{"model":"KBED","paper":"/paper/improving-entity-disambiguation-by-reasoning-1","metrics":{"Micro-F1":"93.4"},"code_links":[{"title":"alexa/refined","url":"https://github.com/alexa/refined"},{"title":"amazon-science/ReFinED","url":"https://github.com/amazon-science/ReFinED"},{"title":"amazon-research/ReFinED","url":"https://github.com/amazon-research/ReFinED"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/uie-on-ace-2004","task":"UIE","dataset_variant":"ACE 2004","rows":1,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"KnowCoder-7b-IE","paper":"/paper/knowcoder-coding-structured-knowledge-into","metrics":{"F1 score":"86.2"},"code_links":[{"title":"ICT-GoKnow/KnowCoder","url":"https://github.com/ICT-GoKnow/KnowCoder"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/to-be-continuous-or-to-be-discrete-those-are","title":"To be Continuous, or to be Discrete, Those are Bits of Questions","date":"2024-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/knowcoder-coding-structured-knowledge-into","title":"KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction","date":"2024-03-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/promptner-prompt-locating-and-typing-for","title":"PromptNER: Prompt Locating and Typing for Named Entity Recognition","date":"2023-05-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/diffusionner-boundary-diffusion-for-named","title":"DiffusionNER: Boundary Diffusion for Named Entity Recognition","date":"2023-05-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/optimizing-bi-encoder-for-named-entity","title":"Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning","date":"2022-08-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-embarrassingly-easy-but-strong-baseline","title":"An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition","date":"2022-08-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/refined-an-efficient-zero-shot-capable-1","title":"ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking","date":"2022-07-08","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/improving-entity-disambiguation-by-reasoning-1","title":"Improving Entity Disambiguation by Reasoning over a Knowledge Base","date":"2022-07-08","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/boundary-smoothing-for-named-entity-1","title":"Boundary Smoothing for Named Entity Recognition","date":"2022-04-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/parallel-instance-query-network-for-named","title":"Parallel Instance Query Network for Named Entity Recognition","date":"2022-03-20","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unified-named-entity-recognition-as-word-word","title":"Unified Named Entity Recognition as Word-Word Relation Classification","date":"2021-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/named-entity-recognition-for-entity-linking","title":"Named Entity Recognition for Entity Linking: What Works and What’s Next","date":"2021-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fusing-heterogeneous-factors-with-triaffine","title":"Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition","date":"2021-10-14","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pack-together-entity-and-relation-extraction","title":"Packed Levitated Marker for Entity and Relation Extraction","date":"2021-09-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-partition-filter-network-for-joint-entity","title":"A Partition Filter Network for Joint Entity and Relation Extraction","date":"2021-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/nested-named-entity-recognition-via","title":"Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path","date":"2021-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/boningknife-joint-entity-mention-detection","title":"BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge","date":"2021-07-20","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/a-unified-generative-framework-for-various","title":"A Unified Generative Framework for Various NER Subtasks","date":"2021-06-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-sequence-to-set-network-for-nested-named","title":"A Sequence-to-Set Network for Nested Named Entity Recognition","date":"2021-05-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/locate-and-label-a-two-stage-identifier-for","title":"Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition","date":"2021-05-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nested-named-entity-recognition-with","title":"Nested Named Entity Recognition with Partially-Observed TreeCRFs","date":"2020-12-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-frustratingly-easy-approach-for-joint","title":"A Frustratingly Easy Approach for Entity and Relation Extraction","date":"2020-10-24","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/two-are-better-than-one-joint-entity-and","title":"Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders","date":"2020-10-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/autoregressive-entity-retrieval","title":"Autoregressive Entity Retrieval","date":"2020-10-02","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/named-entity-recognition-as-dependency","title":"Named Entity Recognition as Dependency Parsing","date":"2020-05-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-unified-mrc-framework-for-named-entity","title":"A Unified MRC Framework for Named Entity Recognition","date":"2019-10-25","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/nested-named-entity-recognition-via-second","title":"Nested Named Entity Recognition via Second-best Sequence Learning and Decoding","date":"2019-09-05","rows_on_this_dataset":5,"code_links":3,"syntology":null},{"paper":"/paper/pre-training-of-deep-contextualized","title":"Global Entity Disambiguation with BERT","date":"2019-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/neural-architectures-for-nested-ner-through-1","title":"Neural Architectures for Nested NER through Linearization","date":"2019-08-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/multi-grained-named-entity-recognition","title":"Multi-Grained Named Entity Recognition","date":"2019-06-20","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/entity-relation-extraction-as-multi-turn","title":"Entity-Relation Extraction as Multi-Turn Question Answering","date":"2019-05-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-general-framework-for-information","title":"A General Framework for Information Extraction using Dynamic Span Graphs","date":"2019-04-05","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/neural-segmental-hypergraphs-for-overlapping","title":"Neural Segmental Hypergraphs for Overlapping Mention Recognition","date":"2018-10-03","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/a-neural-transition-based-model-for-nested","title":"A Neural Transition-based Model for Nested Mention Recognition","date":"2018-10-03","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/adversarial-training-for-multi-context-joint","title":"Adversarial training for multi-context joint entity and relation extraction","date":"2018-08-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-entity-recognition-and-relation","title":"Joint entity recognition and relation extraction as a multi-head selection problem","date":"2018-04-20","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/going-out-on-a-limb-joint-extraction-of","title":"Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees","date":"2017-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-joint-entity-disambiguation-with-local","title":"Deep Joint Entity Disambiguation with Local Neural Attention","date":"2017-04-17","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/end-to-end-relation-extraction-using-lstms-on","title":"End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures","date":"2016-01-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/incremental-joint-extraction-of-entity","title":"Incremental Joint Extraction of Entity Mentions and Relations","date":"2014-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":47,"samples_ran":25,"samples_unverified":22,"pointer_only_for_licence":12,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; 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