tags:
- named-entity-recognition
language:
- ind
- eng
- jav
- min
- sun
- ace
- mly
wikiann
The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used.
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
annotated with LOC (location), PER (person), and ORG (organisation)
tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of
Rahimi et al. (2019), and uses the following subsets from the original WikiANN corpus
Language WikiAnn ISO 639-3
Indonesian id ind
Javanese jv jav
Minangkabau min min
Sundanese su sun
Acehnese ace ace
Malay ms mly
Banyumasan map-bms map-bms
Dataset Usage
Run pip install nusacrowd before loading the dataset through HuggingFace's load_dataset.
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",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/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.",
}
@inproceedings{rahimi-etal-2019-massively,
title = "Massively Multilingual Transfer for {NER}",
author = "Rahimi, Afshin and
Li, Yuan and
Cohn, Trevor",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1015",
pages = "151--164",
}
License
Apache-2.0 license
Homepage
https://github.com/afshinrahimi/mmner
NusaCatalogue
For easy indexing and metadata: https://indonlp.github.io/nusa-catalogue