Language in Vectors: Exploring Word Embedding Key Concepts

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Word embeddings are a type of word representation in Natural Language Processing (NLP) that aims to capture the semantic meaning of words by mapping them to vectors in a continuous vector space. Unlike traditional methods that represent words as discrete symbols or indices, word embeddings provide a distributed representation where the relationships between words are reflected in their vector spaces. Here are key concepts and techniques related to word embeddings:

I. Key Concepts in Word Embeddings:

1. Vector Space Model:

• Word embeddings use a vector space model, where each word is represented as a vector of real numbers. The distance and direction between vectors reflect the semantic relationships between words.

2. Distributional Hypothesis:

• The distributional hypothesis suggests that words with similar meanings tend to occur in similar contexts. Word embeddings leverage this idea by learning representations that capture the contextual similarities between words.

3. Word Similarity and Analogy:

• Word embeddings exhibit semantic properties, allowing them to represent word similarity and analogies. For example, in a…