Update README.md

README.md

@@ -12,28 +12,28 @@ language:
 multilinguality:
 - multilingual
 size_categories:
-- 10K<100k
+- <10K
 task_categories:
 - token-classification
 task_ids:
 - named-entity-recognition
-pretty_name: WikiNeural
+pretty_name: MultiNERD
 ---
 
-# Dataset Card for "tner/wikineural"
+# Dataset Card for "tner/multinerd"
 
 ## Dataset Description
 
 - **Repository:** [T-NER](https://github.com/asahi417/tner)
-- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
-- **Dataset:** WikiNeural
-- **Domain:** Wikipedia
+- **Paper:** [https://aclanthology.org/2022.findings-naacl.60/](https://aclanthology.org/2022.findings-naacl.60/)
+- **Dataset:** MultiNERD
+- **Domain:** Wikipedia, WikiNews
 - **Number of Entity:** 16
 
 
 ### Dataset Summary
-WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
-- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`
+MultiNERD NER benchmark dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`, `SUPER`, `PHY`
 
 ## Dataset Structure
 
@@ -48,7 +48,7 @@ An example of `train` looks as follows.
 ```
 
 ### Label ID
-The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json).
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/multinerd/raw/main/dataset/label.json).
 ```python
 {
 "O": 0,
@@ -82,8 +82,10 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
 "I-TIME": 28,
 "B-VEHI": 29,
 "I-VEHI": 30,
-"B-MISC": 31,
-"I-MISC": 32
+"B-SUPER": 31,
+"I-SUPER": 32,
+"B-PHY": 33,
+"I-PHY": 34
 }
 ```
 
@@ -104,21 +106,18 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
 ### Citation Information
 
 ```
-@inproceedings{tedeschi-etal-2021-wikineural-combined,
-    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
+@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
-      Maiorca, Valentino  and
-      Campolungo, Niccol{\`o}  and
-      Cecconi, Francesco  and
       Navigli, Roberto",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
-    month = nov,
-    year = "2021",
-    address = "Punta Cana, Dominican Republic",
+    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/2021.findings-emnlp.215",
-    doi = "10.18653/v1/2021.findings-emnlp.215",
-    pages = "2521--2533",
-    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
+    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.",
 }
 ```