Update README.md

README.md

@@ -1,3 +1,119 @@
 ---
-license: cc-by-nc-4.0
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+language:
+- de
+- en
+- es
+- fr
+- it
+- nl
+- pl
+- pt
+- ru
+- zh
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- multilingual
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: multinerd-dataset
+tags:
+- structure-prediction
 ---
+
+## Table of Contents
+- [Description](#description)
+- [Dataset Structure](#dataset-structure)
+- [Additional Information](#additional-information)
+  
+## Dataset Card for MultiNERD dataset
+
+## Dataset Description
+
+- **Summary:** Training data for fine-grained NER in 10 languages.
+- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
+- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
+- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com)
+
+
+## Description
+
+- **Summary:** In a nutshell, MultiNERD is the first **language-agnostic** methodology for automatically creating **multilingual, multi-genre and fine-grained annotations** for **Named Entity Recognition** and **Entity Disambiguation**. Specifically, it can be seen an extension of the combination of two prior works from our research group that are [WikiNEuRal](https://www.github.com/Babelscape/wikineural), from which we took inspiration for the state-of-the-art silver-data creation methodology, and [NER4EL](https://www.github.com/Babelscape/NER4EL), from which we took the fine-grained classes and inspiration for the entity linking part. The produced dataset covers: **10 languages** (Chinese, Dutch, English, French, German, Italian, Polish, Portuguese, Russian and Spanish), **15 NER categories** (Person (PER), Location (LOC), Organization (ORG}), Animal (ANIM), Biological entity (BIO), Celestial Body (CEL), Disease (DIS), Event (EVE), Food (FOOD), Instrument (INST), Media (MEDIA), Plant (PLANT), Mythological entity (MYTH), Time (TIME) and Vehicle (VEHI)), and **2 textual genres** ([Wikipedia](https://www.wikipedia.org/) and [WikiNews](https://www.wikinews.org/));
+- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
+- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
+- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com)
+
+## Dataset Structure
+
+The data fields are the same among all splits.
+- `tokens`: a `list` of `string` features.
+- `ner_tags`: a `list` of classification labels (`int`). 
+- `lang`: a `string` feature. Full list of language: Chinese (zh), Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es).
+- The full tagset with indices is reported below:
+```python
+{
+    "O": 0,
+    "B-PER": 1,
+    "I-PER": 2,
+    "B-ORG": 3,
+    "I-ORG": 4,
+    "B-LOC": 5,
+    "I-LOC": 6,
+    "B-ANIM": 7,
+    "I-ANIM": 8,
+    "B-BIO": 9,
+    "I-BIO": 10,
+    "B-CEL": 11,
+    "I-CEL": 12,
+    "B-DIS": 13,
+    "I-DIS": 14,
+    "B-EVE": 15,
+    "I-EVE": 16,
+    "B-FOOD": 17,
+    "I-FOOD": 18,
+    "B-INST": 19,
+    "I-INST": 20,
+    "B-MEDIA": 21,
+    "I-MEDIA": 22,
+    "B-MYTH": 23,
+    "I-MYTH": 24,
+    "B-PLANT": 25,
+    "I-PLANT": 26,
+    "B-TIME": 27,
+    "I-TIME": 28,
+    "B-VEHI": 29,
+    "I-VEHI": 30,
+  }
+```
+
+## Additional Information
+
+- **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
+
+- **Citation Information**: Please consider citing our work if you use data and/or code from this repository.
+```bibtex
+@inproceedings{tedeschi-navigli-2022-multinerd,
+    title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
+    author = "Tedeschi, Simone  and
+      Navigli, Roberto",
+    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-naacl.60",
+    doi = "10.18653/v1/2022.findings-naacl.60",
+    pages = "801--812",
+    abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
+}
+```
+- **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset.
+