Word Embeddings

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.

Word embeddings implementation

Here are the general steps for working with word embeddings in a machine-learning project:

1. Choosing a pretrained model: We choose a pretrained word-embedding model like Word2Vec, GloVe, or FastText to train the embeddings.

2. Preprocessing: We clean and preprocess the text data by performing tasks like word tokenization, removing punctuation, converting text to lowercase, and managing the presence of stopwords.

3. Building vocabulary: We create unique words from the preprocessed text data. This vocabulary lays the foundation for generating meaningful embeddings.

4. Loading pretrained embeddings: We load the embedding matrix and vocabulary from the pretrained model. These components are important for subsequent steps.

5. Generating word embeddings: We map words from the vocabulary to their corresponding pretrained vectors for pretrained embeddings, creating rich word embeddings that capture semantic variations.

6. Integration with models: Incorporate word embeddings into the machine-learning model. This step enhances the model’s understanding of text context and meaning.