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.
- TF-IDF (Term Frequency-Inverse Document Frequency)
- 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.
- 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.
- 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.
- Co-occurrence Matrix
- Context Window: In this approach, a context window is defined around each word in a corpus (e.g., a sentence or a paragraph).
- 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.
- 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.
- 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 in NLP
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.
Table of Content
- Importance of Word Embedding Techniques in NLP
- Word Embedding Techniques in NLP
- 1. Frequency-based Embedding Technique
- 2. Prediction-based Embedding Techniques
- Other Word Embedding Techniques
- FAQs on Word Embedding Techniques
Word embeddings enhance several natural language processing (NLP) steps, such as sentiment analysis, named entity recognition, machine translation, and document categorization.