Explain word embeddings

Theoretical Background:

Word embeddings are compact representations of words in a continuous vector space where semantically similar words are mapped to nearby points. These embeddings help machines understand language patterns by capturing syntactic and semantic word relationships.

Word2Vec was introduced by Mikolov et al. and operates using two primary architectures:

1. Continuous Bag of Words (CBOW): Predicts the target word from surrounding context words.
2. Skip-gram: Predicts context words given a target word, which works better for smaller datasets and captures rare words effectively.

GloVe (Global Vectors for Word Representation): Developed by Pennington et al., GloVe builds on the idea of leveraging the global statistical information of a corpus. It constructs a co-occurrence matrix (i.e., how frequently words appear together) and factorizes it to generate word vectors.

Practical Applications:

• Sentiment Analysis: Understanding the sentiment in text by analyzing embeddings.
• Machine Translation: Translating text from one language to another using semantic similarities.
• Information Retrieval: Enhancing search engines by understanding query context.

Code Example:

from gensim.models import Word2Vec
sentences = [['this', 'is', 'a', 'sentence'], ['another', 'sentence']]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
vector = model.wv['sentence']

External References:

• Mikolov et al., "Efficient Estimation of Word Representations in Vector Space"
• Pennington et al., "GloVe: Global Vectors for Word Representation"

Diagrams:

Here's a diagram illustrating the Skip-gram model in Word2Vec:

graph TD;
    A[Input Word] --> B[Hidden Layer];
    B --> C1[Context Word 1];
    B --> C2[Context Word 2];
    B --> C3[Context Word 3];

The choice between Word2Vec and GloVe often depends on the specific application and dataset characteristics, with Word2Vec being more dynamic for varying contexts and GloVe providing robust, global semantic insights.