WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER

Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, Roberto Navigli


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.
Anthology ID:
2021.findings-emnlp.215
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2521–2533
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.215
DOI:
10.18653/v1/2021.findings-emnlp.215
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.215.pdf
Code
 babelscape/wikineural
Data
WikiNEuRalCoNLL 2002CoNLL-2003