Text-Item Vectorization Task

A Text-Item Vectorization Task is a vectorization task that accepts text items.

• Context:
  • Input: Text Items.
  • output: Text Item Vectors.
    • optional: Text Item Vectorization Structures.
  • It can range from (typically) being a Data-Driven Text Item Vectorization Task (such as corpus-based vectorization) to being a Heuristic Text Item Vectorization Task.
• Example(s):
  • Bag-of-Words Vectorization.
  • Document Vectorization Task.
  • Sentence Vectorization Task.
  • Word Vectorization Task.
• See: Text Item Vectorization System.

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References

2014

• http://scikit-learn.org/stable/modules/feature_extraction.html#the-bag-of-words-representation
  • QUOTE: Text Analysis is a major application field for machine learning algorithms. However the raw data, a sequence of symbols cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.

    ... the most common ways to extract numerical features from text content.

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