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HomeHomework Helpnatural-language-processingEmbedding Techniques in NLP

Embedding Techniques in NLP

Embedding techniques in Natural Language Processing (NLP) are methods used to convert words or phrases into numerical vectors, which capture the semantic meaning of the text and allow for efficient computation in various NLP tasks.

intermediate
3 hours
Natural Language Processing
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Overview

Embedding techniques are essential in Natural Language Processing as they transform words into numerical vectors, enabling machines to understand human language. These techniques include traditional methods like Word2Vec and GloVe, as well as more advanced contextual embeddings like BERT. Understand...

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Key Terms

Word Embedding
A representation of words in a continuous vector space.

Example: In Word2Vec, similar words have similar vectors.

Contextual Embedding
Embeddings that consider the context of words in sentences.

Example: BERT generates different embeddings for 'bank' in 'river bank' and 'financial bank'.

PCA
Principal Component Analysis, a technique for reducing dimensionality.

Example: PCA can simplify a dataset with many features into fewer dimensions.

t-SNE
t-Distributed Stochastic Neighbor Embedding, a technique for visualizing high-dimensional data.

Example: t-SNE can help visualize word embeddings in 2D space.

Transfer Learning
Using a pre-trained model on a new task.

Example: Using BERT trained on a large corpus for a specific sentiment analysis task.

GloVe
Global Vectors for Word Representation, a model for generating word embeddings.

Example: GloVe captures global statistical information of words.

Related Topics

Natural Language Understanding
Focuses on enabling machines to understand human language.
intermediate
Machine Translation
The process of translating text from one language to another using algorithms.
advanced
Sentiment Analysis
Analyzing text to determine the sentiment expressed, such as positive or negative.
intermediate

Key Concepts

Word EmbeddingsContextual EmbeddingsDimensionality ReductionTransfer Learning