Natural Language Processing (NLP) is an area of computer science that focuses on the interaction between human language and computers. One of the fundamental tasks of NLP is to extract relevant information from large volumes of unstructured data. In this article, we will explore one of the most popular techniques used in NLP called TF-IDF.
What is TF-IDF?
TF-IDF is a numerical statistic that reflects the importance of a word in a document. It is commonly used in NLP to represent the relevance of a term to a document or a corpus of documents. The TF-IDF algorithm takes into account two main factors: the frequency of a word in a document (TF) and the frequency of the word across all documents in the corpus (IDF).
The term frequency (TF) is a measure of how frequently a term appears in a document. It is calculated by dividing the number of times a term appears in a document by the total number of words in the document. The resulting value is a number between 0 and 1.
The inverse document frequency (IDF) is a measure of how important a term is across all documents in the corpus. It is calculated by taking the logarithm of the total number of documents in the corpus divided by the number of documents in which the term appears. The resulting value is a number greater than or…
