Update README.md

README.md

@@ -1,171 +1,14 @@
 ---
 language:
-- ace
-- bg
-- da
-- fur
-- ilo
-- lij
-- mzn
-- qu
-- su
-- vi
-- af
-- bh
 - de
-- fy
-- io
-- lmo
-- nap
-- rm
-- sv
-- vls
-- als
-- bn
-- diq
-- ga
-- is
-- ln
-- nds
-- ro
-- sw
-- vo
-- am
-- bo
-- dv
-- gan
-- it
-- lt
-- ne
-- ru
-- szl
-- wa
-- an
-- br
-- el
-- gd
-- ja
-- lv
-- nl
-- rw
-- ta
-- war
-- ang
-- bs
-- eml
-- gl
-- jbo
-- nn
-- sa
-- te
-- wuu
-- ar
-- ca
 - en
-- gn
-- jv
-- mg
-- no
-- sah
-- tg
-- xmf
-- arc
-- eo
-- gu
-- ka
-- mhr
-- nov
-- scn
-- th
-- yi
-- arz
-- cdo
 - es
-- hak
-- kk
-- mi
-- oc
-- sco
-- tk
-- yo
-- as
-- ce
-- et
-- he
-- km
-- min
-- or
-- sd
-- tl
-- zea
-- ast
-- ceb
-- eu
-- hi
-- kn
-- mk
-- os
-- sh
-- tr
-- ay
-- ckb
-- ext
-- hr
-- ko
-- ml
-- pa
-- si
-- tt
-- az
-- co
-- fa
-- hsb
-- ksh
-- mn
-- pdc
-- ug
-- ba
-- crh
-- fi
-- hu
-- ku
-- mr
-- pl
-- sk
-- uk
-- zh
-- bar
-- cs
-- hy
-- ky
-- ms
-- pms
-- sl
-- ur
-- csb
-- fo
-- ia
-- la
-- mt
-- pnb
-- so
-- uz
-- cv
 - fr
-- id
-- lb
-- mwl
-- ps
-- sq
-- vec
-- be
-- cy
-- frr
-- ig
-- li
-- my
+- it
+- nl
+- pl
 - pt
-- sr
+- ru
 multilinguality:
 - multilingual
 size_categories:
@@ -174,18 +17,18 @@ task_categories:
 - token-classification
 task_ids:
 - named-entity-recognition
-pretty_name: WikiAnn
+pretty_name: WikiNeural
 ---
 
-# Dataset Card for "tner/wikiann"
+# Dataset Card for "tner/wikineural"
 
 ## Dataset Description
 
 - **Repository:** [T-NER](https://github.com/asahi417/tner)
-- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
-- **Dataset:** WikiAnn
+- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
+- **Dataset:** WikiNeural
 - **Domain:** Wikipedia
-- **Number of Entity:** 3
+- **Number of Entity:** 16
 
 
 ### Dataset Summary
@@ -205,7 +48,7 @@ An example of `train` looks as follows.
 ```
 
 ### Label ID
-The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/btc/raw/main/dataset/label.json).
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/raw/main/dataset/label.json).
 ```python
 {
     "B-LOC": 0,
@@ -220,29 +63,26 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
 
 ### Data Splits
 
-|  name   |train|validation|test|
-|---------|----:|---------:|---:|
-|btc      | 6338|      1001|2000|
+
 
 ### Citation Information
 
 ```
-@inproceedings{pan-etal-2017-cross,
-    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
-    author = "Pan, Xiaoman  and
-      Zhang, Boliang  and
-      May, Jonathan  and
-      Nothman, Joel  and
-      Knight, Kevin  and
-      Ji, Heng",
-    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = jul,
-    year = "2017",
-    address = "Vancouver, Canada",
+@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}",
+    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",
     publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/P17-1178",
-    doi = "10.18653/v1/P17-1178",
-    pages = "1946--1958",
-    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
+    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.",
 }
 ```