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.
- Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, Roberto Navigli. 2021. WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2521–2533, Punta Cana, Dominican Republic. Association for Computational Linguistics.