Neural Text-Item Embedding Vector

A Neural Text-Item Embedding Vector is a text-item embedding vector that is a neural embedding vector from a neural text-item embedding space.

• Context:
  • It can (often) represent complex Text-Item Semantic Text Features.
  • It can capture nuanced semantic relationships between Text Items.
  • It can be utilized in various Natural Language Processing tasks, such as NLU tasks and NLG tasks.
  • ...
• Example(s):
  • A Method Type-Specific Neural Text-Item Embedding Vector, such as:
    • A Transformer-based Neural Text-Item Embedding Vector (from a transformer-based text-item embedding space).
      • A BERT-based Neural Text-Item Embedding Vector (from a BERT-Based Neural Text-Item Embedding Space).
    • An LSTM-based Neural Text-Item Embedding Vector (from an LSTM-Based Neural Text-Item Embedding Space).
      • An ELMo-based Neural Text-Item Embedding Vector (from an ELMo-Based Neural Text-Item Embedding Space).
    • A Subword Model-based Neural Text-Item Embedding Vector (from a Subword Model-Based Neural Text-Item Embedding Space).
      • A FastText-based Neural Text-Item Embedding Vector (from a FastText-Based Neural Text-Item Embedding Space).
    • ...
  • a Text-Item Type-Specific Neural Text-Item Embedding Vector, such as:
    • a Neural Word Embedding Vector.
    • a Neural Sentence Embedding Vector.
    • a Neural Document Embedding Vector, such as:
      • a Neural Contract Embedding Vector.
      • a Neural Book Embedding Vector.
  • ...
  • ...
  • a Domain-Specific Neural Text-Item Embedding Vector, such as:
    • a Legal Text Neural Embedding Vector (legal text): ...
    • a Medical Text Neural Embedding Vector (medical text): ...
    • ...
  • a Coverage Embedding Text-Item Vector?
  • ...
• Counter-Example(s):
  • a Latent Factor-based Text-Item Embedding Vector.
  • a Sparse Text-Item Vector, which contains many zero values and fewer non-zero values.
• See: Text-Item Vector, Sparse Text-Item Vector, Vector Space Model, Neural Network.

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References

2016

• (Mi et al., 2016) ⇒ Haitao Mi, Baskaran Sankaran, Zhiguo Wang, and Abe Ittycheriah. (2016). “Coverage Embedding Models for Neural Machine Translation." In: arXiv preprint arXiv:1605.03148.
  • QUOTE: ... and introduce a coverage embedding vector for each source ... from a full coverage embedding vector for each source word. ..., • we start with a full embedding vector for each word, instead ...

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