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Found 3 papers, 3 papers with code
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.
Multiple Word Embeddings for Increased Diversity of Representation
Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand.
Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
The CRF layer is used to facilitate global coherence between labels, and the contextual embeddings provide a better representation of words in context.
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