Frequency-based Word Embedding Technique in NLP

Frequency-based embeddings are representations of words in a corpus based on their frequency of occurrence and relationships with other words. Two common techniques for generating frequency-based embeddings are TF-IDF and the co-occurrence matrix.

1. TF-IDF (Term Frequency-Inverse Document Frequency)
   1. Term Frequency (TF): Measures how often a term occurs in a document. It is calculated as the number of times a term appears in a document divided by the total number of terms in the document.
   2. Inverse Document Frequency (IDF): Measures how unique a term is across a collection of documents. It is calculated as the logarithm of the total number of documents divided by the number of documents containing the term.
   3. TF-IDF Weighting: The TF-IDF weight of a term in a document is the product of its TF and IDF values. Terms with high TF-IDF weights are considered more important in the context of the document and the corpus.
2. Co-occurrence Matrix
   1. Context Window: In this approach, a context window is defined around each word in a corpus (e.g., a sentence or a paragraph).
   2. Co-occurrence Matrix: A matrix is constructed where rows and columns represent words, and each cell contains the count of how often a pair of words co-occur within the context window.
   3. Dimension Reduction: Techniques like Singular Value Decomposition (SVD) can be applied to reduce the dimensionality of the co-occurrence matrix and capture latent semantic relationships between words.
   4. Word Similarity: The resulting embeddings can be used to measure the similarity between words based on their co-occurrence patterns in the corpus.

Both TF-IDF and co-occurrence matrix approach are valuable for capturing important relationships between words in a corpus, and they can be used to build representations of words that can be used in various NLP tasks.

Word embedding techniques are a fundamental part of natural language processing (NLP) and machine learning, providing a way to represent words as vectors in a continuous vector space. In this article, we will learn about various word embedding techniques.

Word embeddings enhance several natural language processing (NLP) steps, such as sentiment analysis, named entity recognition, machine translation, and document categorization.