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The Great NLP Showdown: TF-IDF vs GloVe vs Word2Vec vs BERT
Place your bets! Pick your favourites.
When it comes to representing text for machine learning tasks, we have several techniques at our disposal. In this article, we’ll explore four popular methods — TF-IDF, GloVe, Word2Vec, and BERT — by understanding how they work, discussing their advantages and limitations, and comparing their performance using examples.
1. TF-IDF: The Classic Statistical Approach
TF-IDF, or Term Frequency-Inverse Document Frequency, is a statistical method used to represent text data. It evaluates how important a word is to a document in a collection of documents. Here’s how it works:
- Term Frequency (TF): Measures how frequently a term appears in a document.
- Inverse Document Frequency (IDF): Penalizes words that are common across all documents (like “the” or “and”).
The final representation is the product of TF and IDF, giving higher weight to terms that are unique to a document.
Example: Imagine you’re analyzing a set of documents about food. In a document about pizza, the word “pizza” will have a high TF-IDF score because it’s frequent in that document but less frequent across the corpus.
