Update wikineural.py

wikineural.py

@@ -4,43 +4,32 @@ from itertools import chain
 import datasets
 
 logger = datasets.logging.get_logger(__name__)
-_DESCRIPTION = """[WikiAnn](https://aclanthology.org/P17-1178/)"""
-_NAME = "wikiann"
-_VERSION = "1.1.0"
+_DESCRIPTION = """[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)"""
+_NAME = "wikineural"
+_VERSION = "1.0.0"
 _CITATION = """
-@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.",
 }
 """
 
 _HOME_PAGE = "https://github.com/asahi417/tner"
 _URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
-_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", "map-bms", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", "no", "sah", "tg", "xmf",
-        "arc", "cbk-zam", "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", "zh-classical", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt",
-        "zh-min-nan", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "simple", "ug", "zh-yue", "ba", "crh", "fi", "hu",
-        "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "fiu-vro", "hy", "ky", "ms", "pms", "sl", "ur", "bat-smg",
-        "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "be-x-old", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec",
-        "be", "cy", "frr", "ig", "li", "my", "pt", "sr", "vep"]
+_LANGUAGE = ['de', 'en', 'es', 'fr', 'it', 'nl', 'pl', 'pt', 'ru']
 _URLS = {
     l: {
       str(datasets.Split.TEST): [f'{_URL}/{l}/test.jsonl'],
@@ -50,7 +39,7 @@ _URLS = {
 }
 
 
-class WikiAnnConfig(datasets.BuilderConfig):
+class WikiNeuralConfig(datasets.BuilderConfig):
     """BuilderConfig"""
 
     def __init__(self, **kwargs):
@@ -59,14 +48,14 @@ class WikiAnnConfig(datasets.BuilderConfig):
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(WikiAnnConfig, self).__init__(**kwargs)
+        super(WikiNeuralConfig, self).__init__(**kwargs)
 
 
-class WikiAnn(datasets.GeneratorBasedBuilder):
+class WikiNeural(datasets.GeneratorBasedBuilder):
     """Dataset."""
 
     BUILDER_CONFIGS = [
-        WikiAnnConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
+        WikiNeuralConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
     ]
 
     def _split_generators(self, dl_manager):