What is Natural Language Processing?

What is NLP?

• Natural Language Processing (NLP) pertains to the processing, comprehension, and generation of text, encompassing both spoken and written human languages.
• Branches of NLP include Natural Language Understanding (NLU), Information Extraction, Natural Language Generation (NLG), and Automated Speech Recognition (ASR).
• NLP techniques have evolved from bag-of-words models to deep learning with transformer architectures.
• The machine learning process for NLP involves training on labeled and annotated data, tokenization, vectorization, model building, and inference.
• During inference, input text undergoes tokenization, vectorization, prediction by the NLP model, and decoding to obtain desired output.

Quantitative Values and Names of Theories:

• Bag-of-Words (BoW) Model:

  • Advantages:
    • Simple and easy to implement.
    • Effective with short texts and does not require complex natural language processing.
  • Disadvantages:
    • Loses information about order and context.
    • Ineffective with large and complex texts.
  • Evidence:
    • BoW models are commonly used in simple applications like text categorization.

• Deep Learning with Transformer Architectures:

  • Advantages:
    • Effective in processing large and complex texts.
    • Achieves good results on various NLP tasks without extensive fine-tuning.
  • Disadvantages:
    • Requires significant computational resources and substantial training data.
    • Complex and challenging to interpret internal workings.
  • Evidence:
    • Transformer architectures like BERT and GPT-3 have demonstrated impressive results across multiple NLP tasks.

• Tokenization and Vectorization:

  • Advantages:
    • Represent text as numbers to apply machine learning algorithms.
    • Enables the model to understand text and make predictions.
  • Disadvantages:
    • Loss of contextual information and relationships between words and sentences.
    • Sometimes ineffective with complex natural languages.
  • Evidence:
    • Using tokenization and vectorization methods helps NLP models understand the structure and meaning of text.