Flair Word Embedding System

A Flair Word Embedding System is a Character-level Embedding System that creates contextual string embeddings by modeling words as sequences of characters.

• Context:
  • It was first introduced by Akbik et al. (2018).
  • GitHub repositories:
    • https://github.com/zalandoresearch/flair
    • https://github.com/flairNLP/flair
  • It uses a Character-level Language Model and a Sequence tagging model.
• Example(s):
  • Flair tutorials:
    • https://medium.com/analytics-vidhya/practical-approach-of-state-of-the-art-flair-in-named-entity-recognition-46a837e25e6b
    • https://towardsdatascience.com/text-classification-with-state-of-the-art-nlp-library-flair-b541d7add21f
    • …
• Counter-Example(s):
  • BERT System (Devlin et al., 2019),
  • DISSECT System (Dinu et al., 2013),
  • ELMo System (Peters et al., 2018),
  • fastText System (Bojanowski et al., 2017),
  • GenSim System (Rehurek & Sojka, 2010),
  • GloVe System (Pennington et al., 2014),
  • Indra System (Sales et al., 2018),
  • JoBimText System (Biemann & Riedl, 2013),
  • MIMICK System (Pinter et al., 2017),
  • MorphoRNN Embedding System (Luong et al., 2013),
  • Polyglot System (Al-Rfou et al., 2013),
  • SENNA System (Collobert & Weston, 2008),
  • S-Space Word Embedding System (Jurgens & Stevens, 2010),
  • SumEmbed System (Botha & Blunsom, 2014),
  • VarEmbed System (Bhatia et al., 2016),
  • Word2Vec System (Mikolov et al., 2014).
• See: One-Hot Encoding System, DeepLearning4J, Word Similarity Task, Word Analogy Task, Distributional Co-Occurrence Word Vector, Character Embedding System, Graph Embedding System, Subword Embedding System.

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References

• (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).

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