Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914754259320832 |
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| author | Cotterell, Ryan Duh, Kevin |
| author_facet | Cotterell, Ryan Duh, Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields Cotterell, Ryan Duh, Kevin Computation and Language 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. |
| title | Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.09383 |