Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are all closely related subfields within the broader field of Natural Language Technology (NLT).

Natural Language Technology (NLT) is a field that uses various techniques and methods to analyze, understand, and generate human language.
https://oneai.com/learn/natural-language-technology-nlt

• Machine Learning:
  Machine learning is a method that uses algorithms to learn from data and make predictions or decisions. It is widely used in NLT for tasks such as sentiment analysis, named entity recognition, and text summarization.
• Deep Learning:
  Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn from data. It is particularly useful for tasks such as language generation, machine translation, and text classification.
• Rule-Based Systems:
  Rule-based systems use a set of pre-defined rules and heuristics to analyze and understand the text. They are commonly used in tasks such as part-of-speech tagging, syntactic parsing, and named entity recognition.
• Language Modeling:
  Language modeling is a statistical method used to predict the probability distribution of a sequence of words. It is used to train models that can generate text and improve the performance of other NLP tasks such as speech recognition and machine translation.
• Transfer Learning:
  Transfer learning is a method that involves pre-training a neural network on a large dataset and then fine-tuning it on a smaller dataset for a specific task. This approach can be used to improve the performance of tasks such as sentiment analysis, named entity recognition, and language translation.
• Reinforcement Learning:
  Reinforcement learning is a method in which an agent learns by taking actions in an environment to maximize a reward. It is used for tasks such as dialog systems and text generation.

Natural Language Processing (NLP) that is focused on enabling computers to understand human language in both written and verbal forms. It involves machine learning and deep learning techniques.(1) Tokenization, (2) Embedding, and (3) Model architectures, are three main components that help machines understand natural language.

• Language Processing Pipelines
  Text => tokenizer >> tagger > parser > ner > ... => Doc
  • Natural Language Processing | Preprocessing using spaCy
  • Natural Language Processing | Preprocessing using nltk
• Tokenization and vectorization in NLP
  Tokenization is the first step in natural language processing (NLP) projects. It involves dividing a text into individual units, known as tokens. Tokens can be words or punctuation marks. These tokens are then transformed into numerical vectors representing words. Two main concepts are vectorization and embedding. Text Vectorization is the process of turning words into numerical vectors in a one-dimensional space. Word Embedding (Word Vector) is a type of vectorization through deep learning as dense vectors in a high-dimensional space.
  • Tokenization in NLP
    • Sentence based
    • Word based
    • Character based
    • Subwords based
      • BPE Algorithm – a Frequency-based Model
      • WordPiece Algorithm
      • Unigram Algorithm – a Probability-based Model
  • Vectorization in NLP
    • Text Vectorization
      • Traditional approach
        • One-Hot Encoding
        • Bag of Words
        • CountVectorizer
        • TF-IDF
    • Word Embedding
      • Context-independent
        • Neural Word Embedding
          • Word2Vec
        • Pretrained Word-Embedding
          • GloVe
          • FastText
      • Context-dependent
        • RNN based
          • ELMO
          • CoVe
        • Transformer based
          • BERT
          • XML
          • RoBERTa
          • ALBERT
    • Document Embedding
      • Doc2Vec
        • Distributed Memory (DM)
        • Distributed Bag of Words (DBOW)

Natural Language Understanding (NLU) that deals with the ability of computers to understand the meaning of text written in natural language.

Natural Language Generation (NLG) is an advanced Artificial Intelligence technique that transforms non-linguistic representations of information into human-like text.

--updating--

--updating--

--updating--

--updating--

--updating--

--updating--