Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

TL;DR

This paper investigates how cross-lingual transfer can improve Danish Named Entity Recognition (NER) in scenarios with limited annotated data, highlighting its potential to enhance performance where data scarcity exists.

Contribution

It evaluates the effectiveness of cross-lingual transfer for Danish NER and its synergy with limited gold-standard data, addressing data scarcity challenges.

Findings

Cross-lingual transfer improves Danish NER performance.

Limited annotated data can be effectively supplemented by transfer methods.

Insights into Danish NER performance with limited resources.

Abstract

Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.