Word Embeddings
Learn how word embeddings represent words as vectors in a continuous space to capture semantic meaning, context, and relationships in text data. Understand their implementation using pretrained models like Word2Vec, including preprocessing, vocabulary building, and integration into machine learning models. This lesson equips you to enhance your NLP tasks by leveraging rich word vector representations for improved text analysis.
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Introduction
Word embeddings are vector representations of words in a continuous space. They map words to numerical vectors when preparing data for further analysis. These vector representations help us address challenges related to word meaning, context, relationships, and ambiguity that other text representation techniques, such as BoW and TF-IDF, might not address.
Importance of word embeddings
Word embeddings are crucial for several reasons:
Semantic meaning: They capture the semantic meaning of words, i.e., words with similar meanings are represented as vectors close together in the embedding space.
Contextual information: They capture contextual information, i.e., words often appearing in similar contexts will have similar vector representations.
Dimensionality reduction: They reduce the high-dimensional space of words to a lower-dimensional space, making computations more efficient.
Handling out-of-vocabulary words: They can represent words not seen during training by generalizing from similar words. ...