Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
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Abstract (arXiv)
Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages, it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 14 Apr 2024.
Builds on
8 of 31 references.
References · 8
- 2016Neural Architectures for Named Entity Recognitioncited by 548
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
- 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
- 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
- 2017Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networkscited by 30
- 2015Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representationcited by 28
- 2016Multi-task Domain Adaptation for Sequence Taggingcited by 8
- 2013Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learningcited by 1
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.