Update multinerd.py

multinerd.py

@@ -4,26 +4,23 @@ from itertools import chain
 import datasets
 
 logger = datasets.logging.get_logger(__name__)
-_DESCRIPTION = """[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)"""
-_NAME = "wikineural"
+_DESCRIPTION = """[MultiNERD](https://aclanthology.org/2022.findings-naacl.60/)"""
+_NAME = "multinerd"
 _VERSION = "1.0.0"
 _CITATION = """
-@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.",
 }
 """
 
@@ -32,14 +29,12 @@ _URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
 _LANGUAGE = ['de', 'en', 'es', 'fr', 'it', 'nl', 'pl', 'pt', 'ru']
 _URLS = {
     l: {
-      str(datasets.Split.TEST): [f'{_URL}/{l}/test.jsonl'],
-      str(datasets.Split.TRAIN): [f'{_URL}/{l}/train.jsonl'],
-      str(datasets.Split.VALIDATION): [f'{_URL}/{l}/dev.jsonl']
+      str(datasets.Split.TEST): [f'{_URL}/{l}.jsonl'],
      } for l in _LANGUAGE
 }
 
 
-class WikiNeuralConfig(datasets.BuilderConfig):
+class MultiNERDConfig(datasets.BuilderConfig):
     """BuilderConfig"""
 
     def __init__(self, **kwargs):
@@ -48,14 +43,14 @@ class WikiNeuralConfig(datasets.BuilderConfig):
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(WikiNeuralConfig, self).__init__(**kwargs)
+        super(MultiNERDConfig, self).__init__(**kwargs)
 
 
-class WikiNeural(datasets.GeneratorBasedBuilder):
+class MultiNERD(datasets.GeneratorBasedBuilder):
     """Dataset."""
 
     BUILDER_CONFIGS = [
-        WikiNeuralConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
+        MultiNERDConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
     ]
 
     def _split_generators(self, dl_manager):