15 Process Illustrations for Natural Language Processing Models

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In this comprehensive exploration, we delve into various natural language processing (NLP) models, shedding light on their underlying processes through with illustrations.

1. Bag-of-Words (BoW):

• The Bag-of-Words (BoW) model is a simple and widely used technique in natural language processing (NLP) for representing text data.
• It treats a document as an unordered set of words, disregarding grammar and word order, and represents the document by counting the frequency of each word.

   Document
      │
      ▼
┌─────────────┐
│    Token 1  │
└─────────────┘
      │
      ▼
┌─────────────┐
│    Token 2  │
└─────────────┘
      │
      ...
      │
      ▼
┌─────────────┐
│    Token N  │
└─────────────┘
      │
      ▼
┌─────────────┐
│   BoW Vector│
└─────────────┘


Document 1: "This is a simple example."
Document 2: "Another example of Bag-of-Words."

Tokenization:
["This", "is", "a", "simple", "example", "Another", "example", "of", "Bag-of-Words"]

Vocabulary:
["This", "is", "a", "simple", "example", "Another", "of", "Bag-of-Words"]

Word Frequency Count (Document-Term Matrix):
            ["This", "is", "a", "simple", "example", "Another", "of", "Bag-of-Words"]
Document 1:   1        1    1       1        1           0       0           0
Document…