Conditional Random Field (CRF) Decoding Neural Network

A Conditional Random Field (CRF) Decoding Neural Network is a Decoding Neural Network that is based on a Conditional Random Field Structure.

• AKA: CRF Decoder, Neural CRF Decoding Network.
• Example(s):
  • …
• Counter-Example(s):
  • Viterbi-based Decoding System,
  • CRF-based Chunker,
  • Neural CRF Parsing System.
• See: Neural Machine Translation System, Text Error Correction System, WikiText Error Correction System, Seq2Seq Neural Network, Encoder-Decoder Neural Network, Natural Language Processing System, Named Entity Recognition, Part-Of-Speech (PoS) Tagging, Sequence Labeling.

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References

2018

• (Akbik et al., 2018) ⇒ Alan Akbik, Duncan Blythe, and Roland Vollgraf. (2018). “Contextual String Embeddings for Sequence Labeling.” In: Proceedings of the 27th International Conference on Computational Linguistics, (COLING 2018).
  • QUOTE: A large family of NLP tasks such as named entity recognition (NER) and part-of-speech (PoS) tagging may be formulated as sequence labeling problems; text is treated as a sequence of words to be labeled with linguistic tags. Current state-of-the-art approaches for sequence labeling typically use the LSTM variant of bidirectional recurrent neural networks (BiLSTMs), and a subsequent conditional random field (CRF) decoding layer (...).

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