Natural Language Processing
Natural Language Processing (NLP) is a broad subset of artificial intelligence that specialises in language, text, and scripts. NLP applications allow you to process text, analyse it, and generate results that can be used for things like translation, comprehension, and developing enhanced scripts. NLP is not limited to English; it is applicable to a wide range of languages.
Introduction
Learn about the fundamentals and history of Natural Language Processing, as well as the widely used NLP tool kit, NLTK, which is Python-based and contains a plethora of methods and applications for text processing.
NLTK Module
NLTK, or Natural Language Toolkit, is a popular library for Natural Language Processing operations. It includes a number of sub-libraries ranging from
Introduction to Natural Language Processing (NLP)
By soham Medewar
● Published At Feb 2022
In this article, we will discuss the importance of Natural Language Processing. We will study the different ways of text preprocessing essential before training the model.... Keep reading ..
Text Preprocessing
It is always necessary to process any dataset or text source before working on it. Creating a text resource so that it can be further analysed and predictions can be made. This section discusses operations such as spell correction, smoothing, tokenization, stemming, and so on.
Spelling Correction
No NLP engineer or other programmer wants to have unwanted errors in the text source, such as incorrect spelling. Minor spelling errors can cause sign
N-Gram Modelling
By Rajkeshav
● Published At Feb 2022
This blog will focus on one of the simplest machine learning models called the N-Gram model and its implementation. Let's begin.... Keep reading ..
Language Models in NLP
By Prakriti
● Published At May 2022
This article introduces you to different types of Language models and their use.... Keep reading ..
Smoothing in NLP
By Prakriti
● Published At May 2022
In this article, we will learn what smoothing is in NLP, why we need it, and the different techniques for smoothing.... Keep reading ..
Naive Bayes and Laplace Smoothing
By Adya Tiwari
● Published At May 2022
This article explains how Naive Bayes and Laplace Smoothing can be integrated to build a better text classifier and how it will help to tackle the zero probability problem.... Keep reading ..
Computational Morphology
By Rajkeshav
● Published At Mar 2022
This blog will discuss the morphological information that helps us capture the data and the different linguistic terms involved.... Keep reading ..
Tokenization in NLP
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand what is Tokenisation in NLP... Keep reading ..
Stemming in NLP
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand what is Stemming in NLP... Keep reading ..
Chunking in NLP
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand what is Chunking in NLP... Keep reading ..
Lemmatization
By Vamsi Viswanadham
● Published At May 2022
This article gives you a new flavor to the concept of Lemmatization, how it is used to process the text, and how it will help the concept of Natural Language Processing. ... Keep reading ..
Lemmatization with TextBlob
By Siddhant Verma
● Published At May 2022
This article discusses the theoretical knowledge about Lemmatization with TextBlob.... Keep reading ..
Hidden Markov Model
The Hidden Markov Model is an improved markov model that aids in the prediction of unknown variables from known variables. This model can be explained using a graph with directed edges.
PoS Tagging
Part-of-the-speech (PoS), Another important aspect of Natural Language Processing is tagging, which converts speech to words or text based on predefi
Viterbi Decoding with Hidden Markov Models
By Adya Tiwari
● Published At May 2022
This article explains using the Viterbi algorithm with HMM and how it can be used for decoding with the Hidden Markov Models. ... Keep reading ..
Baum Welch Algorithm - HMM
By Vamsi Viswanadham
● Published At May 2022
This article will introduce a basic and top-level overview of the Baum Welch algorithm with the Hidden Markov Model, and this article will also answer why and how to use the Baum Welch algorithm. ... Keep reading ..
Text Classification
Text classification is the process of categorising text, primarily words and sentences, based on the category and sub-category to which they belong. This can be useful for analysing a collection of content that falls into a specific category. Understanding the sentiment of tweets, for example, by analysing them. Learn more about different classification methods.
Syntactic Analysis
Syntactic analysis, also known as parsing, is a Natural Language Processing method of analysing a specific set of words, symbols, phrases, or sentence
Text Classification in NLP
By Mayank Goyal
● Published At May 2022
This article will learn about NLP with its applications; later, we will see the steps involved in Text Classification with its implementation.... Keep reading ..
Maximum Entropy Model
By Mayank Goyal
● Published At Feb 2022
In this article, we will look into MaxEnt and its approach. Later we will study MEMM.... Keep reading ..
Conditional Random Fields
By Lucifer go
● Published At Jun 2022
In this blog, we will learn about conditional random fields in machine learning and their implementation. ... Keep reading ..
Conditional Random Fields
By soham Medewar
● Published At May 2022
In this article, we will discuss the concept of Conditional Random Fields (CRF). Also, will see the maths behind it along with its use cases.... Keep reading ..
Named Entity Recognition
By Rajkeshav
● Published At Apr 2022
In this article, we will talk about NER and its various applications that can be used to detect named entities in texts.... Keep reading ..
NER with KERAS and Tensorflow
By soham Medewar
● Published At May 2022
In this article, we will see the implementation of the name entity recognition model using keras and tensorflow.... Keep reading ..
Semantics
In a corpus of words and sentences, it may be difficult to extract a sequence of structured phrases that can be further analysed and used in Natural Language Processing applications; thus, we employ Semantic Analysis. This is also done with the Lexical database, which is linked to WordNet. Learn about its implementation and the various methods available.
Lexical Semantics
By Mayank Goyal
● Published At May 2022
This article will study semantic analysis with a detailed discussion about lexical semantics. Lastly, we will explore the significance of semantic analysis.... Keep reading ..
WordNet in NLP
By Taneesh Kaushik
● Published At May 2022
In this article, what is Wordnet in NLP. ... Keep reading ..
Word Sense Disambiguation
By Mayank Goyal
● Published At May 2022
In this article, we will learn about WSD, its applications, difficulties faced, different methods to implement WSD and lastly we implemented the same.... Keep reading ..
Synonyms/Antonyms using WordNet
By Akshat Chaturvedi
● Published At May 2022
In this blog, we’ll learn how to generate synonyms and antonyms of a word using WordNet.... Keep reading ..
Sentiment Analysis
Sentiment Analysis is a popular application of Natural Language Processing that predicts the tone, nature, and emotion of any sentence or review in order to determine the overall sentiment of the product or content. This is implemented by a number of behemoths, including Amazon's product reviews and Twitter's Tweet analysis.
Introduction to Sentiment Analysis
By Vamsi Viswanadham
● Published At May 2022
In this article, we are going to cover the basic concepts of sentiment analysis, how it can be used, the few applications of sentiment analysis, etc. ... Keep reading ..
Sentiment Analysis with RNN
By Vamsi Viswanadham
● Published At May 2022
In this article, we can implement Sentiment Analysis using Recurrent Neural Network and LSTM. We will use the Keras library and develop a simple model regarding Sentiment Analysis. ... Keep reading ..
Movie Review Classification Using Sentiment Analysis
By Rajkeshav
● Published At Apr 2022
This blog will use sentiment analysis to focus on movie review classification.... Keep reading ..
Word Embeddings
Word Embedding is a clever algorithm for grouping words with similar meanings or contexts into a vector or group. This is very useful for grouping similar words and further analysing and processing them. Word Embedding can be accomplished using a variety of models, including Word2Vec, GloVe, and the BERT model.
Word2vec Model
Word2Vec is a popular model for vectorizing and embedding words. It is abbreviated from Words to Vectors, as the name suggests. Word2Vec also includes
Glove Embeddings
GloVe is an abbreviation for Global Words for Word Representation. It is yet another word vectorisation and embedding model. It is an unsupervised alg
BERT
BERT is an abbreviation for Bidirectional Encoder Representations from Transformers, which is a transformer-based vectorising and embedding model. It
Word Embeddings in NLP
By Prakriti
● Published At May 2022
In this article, we will learn how we can use text data as input in machine learning algorithms, what Word Embeddings are, and their use.... Keep reading ..
Embedding Layers in Keras
By soham Medewar
● Published At Dec 2021
The following article will introduce you to word embedding in keras and why it plays an essential role in NLP.... Keep reading ..
Topic Modelling
What about a process that extracts a specific set of words from a massive text database or corpus? Because doing it the traditional way would significantly increase complexity, text modelling techniques are implemented that can thread it through simple and smarter models. Discover its architecture, which includes the Latent Dirichlet Allocation.
Topic Modelling with Latent Dirichlet Allocation
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand what is Topic Modelling with Latent Dirichlet Allocation... Keep reading ..
Non Negative Matrix Factorization
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand Nonnegative Matrix Factorization in NLP... Keep reading ..
Gibbs Sampling in LDA
By Adya Tiwari
● Published At May 2022
This article explains Gibbs Sampling used in LDA, the concept with the implementation of how this algorithm can be derived for each latent variable. ... Keep reading ..
NLP Advance
Let's take a look at some advanced techniques, models, and libraries that can be very useful when developing Natural Language Processing applications. Entity Linking, LEXRANK, and frameworks like FLAIR are examples of these.
Entity Linking: A primary NLP task for Information Extraction
By Taneesh Kaushik
● Published At May 2022
In this article, you will understand what is Entity Linking and its applications in NLP. ... Keep reading ..
Information Extraction
By Vamsi Viswanadham
● Published At May 2022
In this article, we will go through the concept of Information Extraction, its uses, and how to implement this concept for better uses using Python.... Keep reading ..
Text Summarization
By Siddhant Verma
● Published At May 2022
In this blog, we will learn about Text Summarization and its various approaches.... Keep reading ..
Text Summarization with TextRank
By Adya Tiwari
● Published At May 2022
We will briefly discuss TextRank and include implementation with Python. Focus on code implementation. ... Keep reading ..
FLAIR – A Framework for NLP
By Taneesh Kaushik
● Published At May 2022
In this article, what is FLAIR and how to use it for NLP. ... Keep reading ..
Hands-on NLP
Learning any technology is meaningless unless its applications are implemented. Learn some hands-on NLP models and algorithms for solving problem statements, such as naming, sentiment analysis, and emotion detection.
Removing Stop Words using NLTK
By Prakriti
● Published At May 2022
This article introduces you to stop words and how they can be removed using the NLTK library.... Keep reading ..
Baby Name Generation with Deep Learning and NLP
By soham Medewar
● Published At May 2022
In this article, we will make a model that will help us in generating new baby names. We will use the concept of deep learning and natural language processing to make this model.... Keep reading ..
Emotion Detection Using Deep Learning
By Mayank Goyal
● Published At May 2022
This article will study emotion detection using Bi-LSTM and implement a code for the same.... Keep reading ..
Restaurant Review Analysis Using NLP
By Rajkeshav
● Published At Mar 2022
This blog will focus on the implementation part of the restaurant review analysis. Let's begin.... Keep reading ..
puzzle icon

Top Problems related to Natural Language Processing