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
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER / Tedeschi, Simone; Maiorca, Valentino; Campolungo, Niccolò; Cecconi, Francesco; Navigli, Roberto. - (2021), pp. 2521-2533. (Intervento presentato al convegno Empirical Methods in Natural Language Processing tenutosi a Punta Cana, Dominican Republic) [10.18653/v1/2021.findings-emnlp.215].
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
Tedeschi, SimonePrimo
;Maiorca, Valentino;Campolungo, Niccolò;Cecconi, Francesco;Navigli, Roberto
2021
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 creationFile | Dimensione | Formato | |
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