What are embeddings in NLP?

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A Long Short-Term Memory (LSTM) network is an advanced type of Recurrent Neural Network (RNN) designed to overcome the limitations of traditional RNNs, especially the vanishing and exploding gradient problems. These problems make it difficult for standard RNNs to learn long-term dependencies in sequential data. LSTMs solve this using a special architecture with a cell state and gates that control the flow of information.

What are Embeddings in NLP?

In Natural Language Processing (NLP), embeddings are a way of representing words, sentences, or documents as numerical vectors (lists of numbers) in a continuous vector space. The goal is to capture the meaning and context of language in a mathematical form that computers can understand and process.

Traditional NLP methods used to treat words as independent symbols (like word IDs), which failed to capture relationships between them. For example, “king” and “queen” would just be two unrelated IDs. Embeddings solve this by placing semantically similar words close to each other in vector space.

Key Features of Embeddings:

1. Semantic Similarity – Words with similar meanings have vectors that are close to each other (e.g., dog and puppy).

2. Context Capture – Advanced embeddings (like BERT, GPT) represent words differently depending on context. For example, the word bank in river bank vs. money bank.

3. Dimensionality Reduction – Instead of dealing with sparse, huge vectors (like one-hot encoding), embeddings are compact and efficient.

Common Types of Embeddings:

• Word2Vec, GloVe, FastText – Static word embeddings that assign one fixed vector per word.

• Contextual Embeddings (BERT, GPT, ELMo) – Dynamic embeddings that change meaning based on context.

Uses of Embeddings in NLP:

• Text classification (spam detection, sentiment analysis)

• Machine translation

• Search engines & information retrieval

• Chatbots and virtual assistants

• Recommendation systems

👉 In short: Embeddings are numerical vector representations of text that capture meaning, relationships, and context, making it possible for machines to understand and work with human language.

🔑Read More:

What is the vanishing gradient problem?

Explain RNN (Recurrent Neural Network).

What is LSTM, and how does it work?

What is transfer learning?

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