Update README.md

README.md

@@ -98,4 +98,176 @@ configs:
     path: raw/validation-*
   - split: test
     path: raw/test-*
+language:
+- en
+tags:
+- news
+- relation-extraction
+pretty_name: NYT-multi
+size_categories:
+- 10K<n<100K
 ---
+# Dataset Card for NYT-multi
+
+## Dataset Description
+
+- **Repository:** https://github.com/xiangrongzeng/copy_re
+- **Paper:** https://aclanthology.org/P18-1047/
+
+#### Dataset Summary
+
+<!-- Provide a quick summary of the dataset. -->
+
+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/
+
+The version here is mainly based on the work by 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.
+The 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. You can access the raw version from Zeng et al. (2018) using `datasets.load_dataset("DFKI-SLT/nyt-multi", config="raw")`.
+
+### Languages
+
+The language in the dataset is English.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+### 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
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**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)