--- dataset_info: - config_name: default features: - name: tokens sequence: string - name: spo_list sequence: sequence: string - name: pos_tags sequence: string - name: relations list: - name: h struct: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: type dtype: string - name: t struct: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: type dtype: string - name: type dtype: string splits: - name: train num_bytes: 48934795 num_examples: 56196 - name: validation num_bytes: 4369341 num_examples: 5000 - name: test num_bytes: 4395817 num_examples: 5000 download_size: 14425744 dataset_size: 57699953 - config_name: raw features: - name: sentText dtype: string - name: articleId dtype: string - name: relationMentions list: - name: em1Text dtype: string - name: em2Text dtype: string - name: label dtype: string - name: entityMentions list: - name: start dtype: int64 - name: label dtype: string - name: text dtype: string - name: sentId dtype: string splits: - name: train num_bytes: 29397404 num_examples: 56196 - name: validation num_bytes: 2625955 num_examples: 5000 - name: test num_bytes: 2629885 num_examples: 5000 download_size: 13342957 dataset_size: 34653244 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: raw data_files: - split: train path: raw/train-* - split: validation path: raw/validation-* - split: test path: raw/test-* language: - en tags: - news - relation-extraction pretty_name: NYT-multi size_categories: - 10K The original NYT dataset by Riedel et al. (2010) consists of New York Times news articles from 1987-2007 that was distantly annotated with relations using FreeBase. The original dataset consisted of 1.18M sentences. It is available here: https://iesl.cs.umass.edu/riedel/ecml/ Zeng et al. (2018) that filtered out sentences with more than 100 words and sentences without an active relation, leaving 66195 sentences. They randomly selected 5000 sentences from it as the test set, 5000 sentences as the validation set and the rest 56195 sentences are used as train set. The resulting dataset called NYT-multi features overlapping entities across three entity types and 24 relation types. You can access the raw version from Zeng et al. (2018) using `datasets.load_dataset("DFKI-SLT/nyt-multi", config="raw")`. The original data is available here: https://github.com/xiangrongzeng/copy_re The data was further pre-processed with the StanfordCoreNLP by Yu et al. (2020): https://github.com/yubowen-ph/JointER We converted the data into a more readable JSON format and used it for the default version of the dataset. ### Languages The language in the dataset is English. ## Dataset Structure ### Dataset Instances #### default An example of 'train' looks as follows: ```json { "tokens": ["Massachusetts", "ASTON", "MAGNA", "Great", "Barrington", ";", "also", "at", "Bard", "College", ",", "Annandale-on-Hudson", ",", "N.Y.", ",", "July", "1-Aug", "."], "spo_list": [["Annandale-on-Hudson", "/location/location/contains", "Bard College"]], "pos_tags": ["NNP", "NNP", "NNP", "NNP", "NNP", ":", "RB", "IN", "NNP", "NNP", ",", "NNP", ",", "NNP", ",", "NNP", "NNP", "."], "relations": [ { "h": {"text": "Annandale-on-Hudson", "start": 11, "end": 12, "type": "LOCATION"}, "t": {"text": "Bard College", "start": 8, "end": 10, "type": "ORGANIZATION"}, "type": "/location/location/contains" } ] } ``` ### raw An example of 'train' looks as follows: ```json { "sentText": "Massachusetts ASTON MAGNA Great Barrington ; also at Bard College , Annandale-on-Hudson , N.Y. , July 1-Aug .", "articleId": "/m/vinci8/data1/riedel/projects/relation/kb/nyt1/docstore/nyt-2005-2006.backup/1669365.xml.pb", "relationMentions": [ {"em1Text": "Annandale-on-Hudson", "em2Text": "Bard College", "label": "/location/location/contains"} ], "entityMentions": [ {"start": 1, "label": "ORGANIZATION", "text": "Bard College"}, {"start": 2, "label": "LOCATION", "text": "Annandale-on-Hudson"} ], "sentId": "1" } ``` ### Data Fields ### default - `tokens`: the tokenized text of this example, a `list` of `string` features. - `spo_list`: the relation triplets (head entity text, relation type, tail entity text), a `list` of `list`s containing `string` features. - `pos_tags`: the part-of-speech tags of this example, a `list` of `string` features. - `relations`: list of relations - `h`: the head entity - `text`: the entity text, a `string` feature. - `start`: start index of the head entity, a `int32` feature. - `end`: end index of the head entity, a `int32` feature. - `type`: the entity type, a `string` feature. - `t`: the tail entity - `text`: the entity text, a `string` feature. - `start`: start index of the tail entity, a `int32` feature. - `end`: end index of the tail entity, a `int32` feature. - `type`: the entity type, a `string` feature. - `type`: relation type, a `string` feature. ### raw - `sentText`: the text of this example, a `string` feature. - `articleId`: the id of the article, a `string` feature. - `relationMentions`: list of relation mentions - `em1Text`: the head entity text, a `string` feature. - `em2Text`: the tail entity text, a `string` feature. - `label`: relation type, a `string` feature. - `entityMentions`: list of entity mentions - `start`: start index of the tail entity, a `int32` feature. - `label`: the entity type, a `string` feature. - `text`: the entity text, a `string` feature. - `sentId`: index of the sentence, a `string` feature ## Citation **BibTeX:** ``` @inproceedings{zeng-etal-2018-extracting, title = "Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism", author = "Zeng, Xiangrong and Zeng, Daojian and He, Shizhu and Liu, Kang and Zhao, Jun", editor = "Gurevych, Iryna and Miyao, Yusuke", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P18-1047", doi = "10.18653/v1/P18-1047", pages = "506--514", abstract = "The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.", } @article{yu-etal-2019-joint, author = {Bowen Yu and Zhenyu Zhang and Jianlin Su and Yubin Wang and Tingwen Liu and Bin Wang and Sujian Li}, title = {Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy}, journal = {CoRR}, volume = {abs/1909.04273}, year = {2019}, url = {http://arxiv.org/abs/1909.04273}, eprinttype = {arXiv}, eprint = {1909.04273}, timestamp = {Mon, 24 Aug 2020 08:57:29 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-04273.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @inproceedings{riedel2010modeling, title={Modeling relations and their mentions without labeled text}, author={Riedel, Sebastian and Yao, Limin and McCallum, Andrew}, booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21}, pages={148--163}, year={2010}, organization={Springer} } ``` **APA:** - Zeng, X., Zeng, D., He, S., Liu, K., & Zhao, J. (2018, July). Extracting relational facts by an end-to-end neural model with copy mechanism. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 506-514). - Yu, B., Zhang, Z., Su, J., Wang, Y., Liu, T., Wang, B., & Li, S. (2019). Joint extraction of entities and relations based on a novel decomposition strategy. CoRR, abs/1909.04273. Retrieved from http://arxiv.org/abs/1909.04273 - Riedel, S., Yao, L., & McCallum, A. (2010). Modeling relations and their mentions without labeled text. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III 21 (pp. 148-163). Springer Berlin Heidelberg. ## Dataset Card Authors [@phucdev](https://github.com/phucdev)