Introduction to Word and Sentence Embedding

In the field of Natural Language Processing (NLP), the use of word and sentence embeddings has revolutionized the way we analyze and understand language. Word embeddings and sentence embeddings are numerical representations of words and sentences, respectively, that capture the underlying semantics and meaning of the text.

In this blog post, we will discuss what word and sentence embeddings are, how they are created, and how they can be used in NLP tasks. We will also provide some Python code examples to illustrate the concepts.

P.S. This article was originally published on AI-ContentLab:

Word Embeddings:

A word embedding is a way of representing words as high-dimensional vectors. These vectors capture the meaning of a word based on its context in a given text corpus. The most commonly used approach to creating word embeddings is through the use of neural networks, particularly the Word2Vec algorithm.

The Word2Vec algorithm is a neural network model that learns word embeddings by predicting the context in which a word appears. The model takes a large corpus of text as input and creates a vector representation for each word in the vocabulary. The idea behind the model is that words that appear in similar contexts tend to have similar meanings.