Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
relation extraction
License:
| annotations_creators: | |
| - crowdsourced | |
| - expert-generated | |
| language: | |
| - en | |
| language_creators: | |
| - found | |
| license: | |
| - other | |
| multilinguality: | |
| - monolingual | |
| pretty_name: tacred | |
| size_categories: | |
| - 100K<n<1M | |
| source_datasets: | |
| - extended|other | |
| tags: | |
| - relation extraction | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| # Dataset Card for "tacred" | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred) | |
| - **Paper:** [Position-aware Attention and Supervised Data Improve Slot Filling](https://aclanthology.org/D17-1004/) | |
| - **Point of Contact:** See [https://nlp.stanford.edu/projects/tacred/](https://nlp.stanford.edu/projects/tacred/) | |
| - **Size of downloaded dataset files:** 62.3 MB | |
| - **Size of the generated dataset:** 139.2 MB | |
| - **Total amount of disk used:** 201.5 MB | |
| ### Dataset Summary | |
| The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge | |
| Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended | |
| and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC | |
| KBP challenges and crowdsourcing. Please see our EMNLP paper, or our EMNLP slides for full details. | |
| Note: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of | |
| the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/) | |
| published at ACL 2020. | |
| ### Supported Tasks and Leaderboards | |
| - **Tasks:** Relation Classification | |
| - **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-tacred](https://paperswithcode.com/sota/relation-extraction-on-tacred) | |
| ### Languages | |
| The language in the dataset is English. | |
| ## Dataset Structure | |
| ### Data Instances | |
| - **Size of downloaded dataset files:** 62.3 MB | |
| - **Size of the generated dataset:** 139.2 MB | |
| - **Total amount of disk used:** 201.5 MB | |
| An example of 'train' looks as follows: | |
| ```json | |
| { | |
| "id": "61b3a5c8c9a882dcfcd2", | |
| "docid": "AFP_ENG_20070218.0019.LDC2009T13", | |
| "relation": "org:founded_by", | |
| "tokens": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."], | |
| "subj_start": 10, | |
| "subj_end": 13, | |
| "obj_start": 0, | |
| "obj_end": 2, | |
| "subj_type": "ORGANIZATION", | |
| "obj_type": "PERSON", | |
| "pos_tags": ["NNP", "NNP", "VBD", "IN", "NNP", "JJ", "NN", "TO", "VB", "DT", "DT", "NNP", "NNP", "-LRB-", "NNP", "-RRB-", ",", "VBG", "DT", "NN", "IN", "CD", "NNS", "IN", "NN", ",", "VBG", "JJ", "NN", "NNP", "NNP", "NNP", "TO", "VB", "NN", "CC", "VB", "DT", "NN", "NN", "."], | |
| "ner_tags": ["PERSON", "PERSON", "O", "O", "DATE", "DATE", "DATE", "O", "O", "O", "O", "O", "O", "O", "ORGANIZATION", "O", "O", "O", "O", "O", "O", "NUMBER", "O", "O", "O", "O", "O", "O", "O", "O", "PERSON", "PERSON", "O", "O", "O", "O", "O", "O", "O", "O", "O"], | |
| "stanford_head": [2, 3, 0, 5, 3, 7, 3, 9, 3, 13, 13, 13, 9, 15, 13, 15, 3, 3, 20, 18, 23, 23, 18, 25, 23, 3, 3, 32, 32, 32, 32, 27, 34, 27, 34, 34, 34, 40, 40, 37, 3], | |
| "stanford_deprel": ["compound", "nsubj", "ROOT", "case", "nmod", "amod", "nmod:tmod", "mark", "xcomp", "det", "compound", "compound", "dobj", "punct", "appos", "punct", "punct", "xcomp", "det", "dobj", "case", "nummod", "nmod", "case", "nmod", "punct", "xcomp", "amod", "compound", "compound", "compound", "dobj", "mark", "xcomp", "dobj", "cc", "conj", "det", "compound", "dobj", "punct"] | |
| } | |
| ``` | |
| ### Data Fields | |
| The data fields are the same among all splits. | |
| - `id`: the instance id of this sentence, a `string` feature. | |
| - `docid`: the TAC KBP document id of this sentence, a `string` feature. | |
| - `tokens`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit, a `list` of `string` features. | |
| - `relation`: the relation label of this instance, a `string` classification label. | |
| - `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature. | |
| - `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature. | |
| - `subj_type`: the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature. | |
| - `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature. | |
| - `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature. | |
| - `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature. | |
| - `pos_tags`: the part-of-speech tag per token. the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features. | |
| - `ner_tags`: the NER tags of tokens (IO-Scheme), among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features. | |
| - `stanford_deprel`: the Stanford dependency relation tag per token, a `list` of `string` features. | |
| - `stanford_head`: the head (source) token index (0-based) for the dependency relation per token. The root token has a head index of -1, a `list` of `int` features. | |
| ### Data Splits | |
| To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run: | |
| | | Train | Dev | Test | | |
| | ----- | ------ | ----- | ---- | | |
| | TACRED | 68,124 (TAC KBP 2009-2012) | 22,631 (TAC KBP 2013) | 15,509 (TAC KBP 2014) | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| See the Stanford paper and the Tacred Revisited paper, plus their appendices. | |
| To ensure that models trained on TACRED are not biased towards predicting false positives on real-world text, | |
| all sampled sentences where no relation was found between the mention pairs were fully annotated to be negative examples. As a result, 79.5% of the examples | |
| are labeled as no_relation. | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| To respect the copyright of the underlying TAC KBP corpus, TACRED is released via the | |
| Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)). | |
| You can download TACRED from the [LDC TACRED webpage](https://catalog.ldc.upenn.edu/LDC2018T24). | |
| If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed. | |
| ### Citation Information | |
| The original dataset: | |
| ``` | |
| @inproceedings{zhang2017tacred, | |
| author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.}, | |
| booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, | |
| title = {Position-aware Attention and Supervised Data Improve Slot Filling}, | |
| url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf}, | |
| pages = {35--45}, | |
| year = {2017} | |
| } | |
| ``` | |
| For the revised version, please also cite: | |
| ``` | |
| @inproceedings{alt-etal-2020-tacred, | |
| title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task", | |
| author = "Alt, Christoph and | |
| Gabryszak, Aleksandra and | |
| Hennig, Leonhard", | |
| booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", | |
| month = jul, | |
| year = "2020", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/2020.acl-main.142", | |
| doi = "10.18653/v1/2020.acl-main.142", | |
| pages = "1558--1569", | |
| } | |
| ``` | |
| ### Contributions | |
| #Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |