Natural Language Processing

This post will take you to a basic understanding of concepts of simple feature extraction methods in Natural Language Processing. Need of Such Models to check similarity between sentences Machine learning algorithms cannot work with the raw data directly the text must be converted into numbers. Especially, vectors of numbers Bag of Words (BOW) The Bag-of-words model (BoW ) is the simplest way of extracting features from the text. BoW converts text into the matrix of occurrence of words within a document. This model concerns whether given words occurred or not in the document. The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification. Bag of Words involves two steps: The vocabulary of words : This step involves constructing a document corpus which consists of all unique words of the text present in the data provided. Example: If we are given 4 reviews for veg Italian pizza