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We describe a multifaceted approach to named entity recognition that can be deployed with minimal data resources and a handful of hours of non-expert annotation. We describe how this approach… Click to show full abstract
We describe a multifaceted approach to named entity recognition that can be deployed with minimal data resources and a handful of hours of non-expert annotation. We describe how this approach was applied in the 2016 LoReHLT evaluation and demonstrate that both statistical and rule-based approaches contribute to our performance. We also demonstrate across many languages the value of selecting the sentences to be annotated when training on small amounts of data.
Keywords:
rule based;
named entity;
entity recognition
Journal Title: Machine Translation
Year Published: 2017
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Journal Title: Machine Translation
Year Published: 2017
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!