Deep Learning Techniques in NLP: Embeddings and Neural Networks

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🚀 Deep Learning in NLP: Word Embeddings & Neural Networks

Deep learning revolutionized NLP through the use of word embeddings and neural networks, bringing semantic understanding closer to human-level performance.

🧠 Word Embeddings

Techniques like:

  • 🔤 Word2Vec
  • 🧮 GloVe
  • 🔡 FastText

convert words into dense vectors that capture semantic relationships such as similarity and contextual nuance.

These embeddings allow models to:

  • Recognize word similarity (e.g., king ≈ queen)
  • Understand relationships (e.g., Paris - France ≈ Berlin - Germany)

🤖 Neural Networks for NLP

Modern neural architectures, especially transformer-based models, process these embeddings to perform tasks like:

  • 🌍 Translation
  • 📄 Summarization
  • 📊 Classification

💻 Example: Using Word2Vec in Python

from gensim.models import Word2Vec

sentences = [['natural', 'language', 'processing'], ['machine', 'learning', 'is', 'fun']] 
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)

print(model.wv['language'])

🔍 Why It Matters

This approach enables models to leverage contextual semantics, making NLP systems more human-like and accurate.

🧩 Diagram: Word Embedding to NLP Task

Raw Text
   |
   v
[Tokenization]
   |
   v
[Word Embedding (Word2Vec / GloVe)]
   |
   v
[Neural Network / Transformer]
   |
   v
[NLP Task (e.g. Translation, Sentiment Analysis)]