1. Introduction Before we start implementing N-Grams, let’s first understand what N-Grams are and why they are important in Natural Language Processing (NLP). In NLP, an N-Gram is a sequence of N consecutive words (or tokens) from a given text. It is a simple yet powerful way to represent and analyze text data based on…

1. Introduction In Natural Language Processing (NLP), one of the fundamental challenges is understanding the relationship between words in a sentence. Simple models like Bag of Words (BoW) treat every word independently and ignore how words are positioned or related to each other. For example, in the sentences: Both sentences contain the same words, but…

1. Introduction Bag of Words (BoW) is a simple and widely used text representation technique in Natural Language Processing (NLP). It transforms text documents into numerical feature vectors by counting the occurrences of words. The name “Bag of Words” comes from the idea that the text is treated as a bag of words, ignoring grammar,…

1. Introduction to Bag of Words In Natural Language Processing, we often need to convert text data into numerical form so that machine learning models can understand it.The Bag of Words (BoW) model is one of the simplest and most intuitive techniques for this purpose. BoW represents a text (like a sentence, paragraph, or document)…

1. Introduction to One-Hot Encoding One-Hot Encoding (OHE) is a data preprocessing technique used to convert categorical data into a numerical format that can be fed into machine learning models. Machine learning algorithms like Linear Regression, Logistic Regression, SVM, or Neural Networks cannot directly understand text labels such as “Red” or “Blue”.They require numerical input.…

1. Introduction to Named Entity Recognition (NER) Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that identifies specific types of entities in text and classifies them into predefined categories such as: For example: 2. Why NER Is Important Most real-world data—news, reports, and emails—is unstructured. NER transforms this text into structured…

1. Introduction In Natural Language Processing (NLP), Part-of-Speech (POS) Tagging is the process of assigning grammatical labels (like noun, verb, adjective, etc.) to each word in a sentence. For example, in the sentence:“Arjun is learning NLP.”the words can be tagged as: POS tagging provides important syntactic information that helps NLP systems understand how words relate…

1. Introduction When working with text data, not all words contribute equally to understanding the meaning of a sentence.Words like “is”, “the”, “in”, “at”, “of”, “an”, and “and” occur very frequently in text but usually do not carry important semantic meaning for NLP tasks such as classification, sentiment analysis, or topic modeling. These words are…

1. Introduction When working with textual data in Natural Language Processing (NLP), it’s crucial to reduce words to their base or root form. This process ensures that words with similar meanings are treated alike by algorithms. While stemming reduces words by chopping off prefixes or suffixes using rules, it often produces non-dictionary stems (like “studying”…

1. Introduction In Natural Language Processing (NLP), text preprocessing is a crucial step before feeding data into machine learning or deep learning models. One important part of preprocessing is stemming, which is the process of reducing words to their root or base form. For example: Notice that the stemmed form may not always be a…