Word Sense Disambiguation: Importance in Natural Language Processing

This article was published as a part of the Data Science Blogathon Introduction In human language, often a word is used in more than one way. Understanding the various usage patterns in the language is important for various Natural Language Processing Applications. ( Image: https://www.pexels.com/photo/book-eyeglasses-eyewear-page-261857/ ) In various usage situations, the same word can mean differently. As, a vast majority of the information online, is in English, for the sake of simplicity, let us deal with examples in the English language only. […]

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How to Perform Basic Text Analysis without Training Dataset

This article was published as a part of the Data Science Blogathon Overview This article will give you a basic understanding of how text analysis works. Learn the various steps of the NLP pipeline Derivation of the overall sentiment of the text. Dashboard depicting the general statistics and sentiment analysis of the text. Abstract In this modern digital era, a large amount of information is generated per second. Most of the data humans generate through WhatsApp messages, tweets, blogs, news articles, […]

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Part 14: Step by Step Guide to Master NLP – Basics of Topic Modelling

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In this series, we completed our discussion on the entity extraction technique “Named Entity Recognition (NER)”. But at that time, we didn’t discuss another popular entity extraction technique called Topic Modelling. So, in continuation of that article, we will discuss Topic modelling in this article. In this article, we will discuss firstly some of […]

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How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models

Overview The Transformer model in NLP has truly changed the way we work with text data Transformer is behind the recent NLP developments, including Google’s BERT Learn how the Transformer idea works, how it’s related to language modeling, sequence-to-sequence modeling, and how it enables Google’s BERT model   Introduction I love being a data scientist working in Natural Language Processing (NLP) right now. The breakthroughs and developments are occurring at an unprecedented pace. From the super-efficient ULMFiT framework to Google’s […]

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An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

Introduction Before we start, have a look at the below examples. You open Google and search for a news article on the ongoing Champions trophy and get hundreds of search results in return about it. Nate silver analysed millions of tweets and correctly predicted the results of 49 out of 50 states in 2008 U.S Presidential Elections. You type a sentence in google translate in English and get an Equivalent Chinese conversion.   So what do the above examples have […]

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An Introductory Guide to Understand how ANNs Conceptualize New Ideas (using Embedding)

Introduction Here’s something you don’t hear everyday – everything we perceive is just a best case probabilistic prediction by our brain, based on our past encounters and knowledge gained through other mediums. This might sound extremely counter intuitive because we have always imagined that our brain mostly gives us deterministic answers. We’ll do a small experiment to showcase this logic. Take a look at the below image: Q1. Do you see a human ? Q2. Can you identify the person? […]

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What is Tokenization in NLP? Here’s All You Need To Know

Highlights Tokenization is a key (and mandatory) aspect of working with text data We’ll discuss the various nuances of tokenization, including how to handle Out-of-Vocabulary words (OOV)   Introduction Language is a thing of beauty. But mastering a new language from scratch is quite a daunting prospect. If you’ve ever picked up a language that wasn’t your mother tongue, you’ll relate to this! There are so many layers to peel off and syntaxes to consider – it’s quite a challenge. […]

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An Introduction to Text Summarization using the TextRank Algorithm (with Python implementation)

Introduction Text Summarization is one of those applications of Natural Language Processing (NLP) which is bound to have a huge impact on our lives. With growing digital media and ever growing publishing – who has the time to go through entire articles / documents / books to decide whether they are useful or not? Thankfully – this technology is already here. Have you come across the mobile app inshorts? It’s an innovative news app that converts news articles into a […]

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Build a Natural Language Generation (NLG) System using PyTorch

Overview Introduction to Natural Language Generation (NLG) and related things- Data Preparation Training Neural Language Models Build a Natural Language Generation System using PyTorch Introduction In the last few years, Natural language processing (NLP) has seen quite a significant growth thanks to advancements in deep learning algorithms and the availability of sufficient computational power. However, feed-forward neural networks are not considered optimal for modeling a language or text. This is because the feed-forward network does not take into consideration the […]

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Essentials of Deep Learning : Introduction to Long Short Term Memory

Introduction Sequence prediction problems have been around for a long time. They are considered as one of the hardest problems to solve in the data science industry. These include a wide range of problems; from predicting sales to finding patterns in stock markets’ data, from understanding movie plots to recognizing your way of speech, from language translations to predicting your next word on your iPhone’s keyboard. With the recent breakthroughs that have been happening in data science, it is found […]

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