June 28, 2021 Advanced, NLP, Text

Part 18: Step by Step Guide to Master NLP – Topic Modelling using LDA (Probabilistic Approach)

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 the previous part of this series, we completed our discussion on pLSA, which is a probabilistic framework for Topic Modelling. But we have seen some of the limitations of pLSA, so to resolve those limitations LDA comes into the picture. So, In this article, we will discuss the probabilistic or Bayesian approach to […]

June 27, 2021 Beginner, Machine Learning, NLP, Unstructured Data, Unsupervised

Topic Modeling and Latent Dirichlet Allocation(LDA) using Gensim and Sklearn : Part 1

This article was published as a part of the Data Science Blogathon Introduction Let’s say you have a client who has a publishing house. Your client comes to you with two tasks: one he wants to categorize all the books or the research papers he receives weekly on a common theme or a topic and the other task is to encapsulate large documents into smaller bite-sized texts. Is there any technique and tool available that can do both of these two […]

June 26, 2021 Beginner, Data Science, NLP, Python, Text, Word Embeddings

Part 8: Step by Step Guide to Master NLP – Useful Natural Language Processing Tasks

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). Up to part-7 of this series, we completed the most useful concepts in NLP. While going away in this series, let’s first discuss some of the useful tasks of NLP so that you have much clarity about what you can do by learning the NLP. After this part, we will start our discussion on […]

June 26, 2021 Advanced, NLP

Part 11: Step by Step Guide to Master NLP – Syntactic Analysis

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 the previous article, we discussed an entity extraction technique named i.e, Named Entity Recognition. There is also another entity extraction technique which is also a popular technique named Topic Modeling, which we will discuss in the subsequent articles of our blog series. So, In this article, we will deep dive into Syntactic Analysis, […]

June 26, 2021 Advanced, Data Engineering, Docker, Machine Learning, Model Deployment, NLP, Profile Building

Deploying Machine learning Application on AWS Fargate

Amazon Web Services(AWS) offers reliable, scalable, and cost-effective cloud computing services. It provides Infrastructure as a service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS) also a new model known as Function as a Service(FaaS) eg. AWS Lambda which is a serverless entity. Before going further, if you don’t have an AWS account please create one to follow along with the hands-on. Amazon Elastic Container Service(ECS) Amazon ECS is a container orchestration platform developed by Amazon, it is similar to […]

June 25, 2021 Deep Learning, Libraries, NLP, Python, Unsupervised

Implementing Transformers in NLP Under 5 Lines Of Codes

This article was published as a part of the Data Science Blogathon Introduction Today, we will see a gentle introduction to the transformers library for executing state-of-the-art models for complex NLP tasks. Applying state-of-the-art Natural Language Processing models has never been more straightforward. Hugging Face has revealed a compelling library called transformers that allow us to perform and use a broad class of state-of-the-art NLP models in a specific way. Today we are operating to install and use the transformers library […]

June 25, 2021 Advanced, NLP, Project, Python, Structured Data, Text

Text Preprocessing made easy!

This article was published as a part of the Data Science Blogathon Introduction We will learn the basics of text preprocessing in this article. Humans communicate using words and hence generate a lot of text data for companies in the form of reviews, suggestions, feedback, social media, etc. A lot of valuable insights can be generated from this text data and hence companies try to apply various machine learning or deep learning models to this data to gain actionable insights. Text […]

June 25, 2021 Advanced, NLP, Project, Python, Structured Data, Text

NLP Application: Named Entity Recognition (NER) in Python with Spacy

Natural Language Processing deals with text data. The amount of text data generated these days is enormous. And, this data if utilized properly can bring many fruitful results. Some of the most important Natural Language Processing applications are Text Analytics, Parts of Speech Tagging, Sentiment Analysis, and Named Entity Recognition. The vast amount of text data contains a huge amount of information. An important aspect of analyzing these text data is the identification of Named Entities. What is a Named […]

June 25, 2021 Advanced, Data Science, NLP, Python, Text

A Gentle Introduction To MuRIL : Multilingual Representations for Indian Languages

This article was published as a part of the Data Science Blogathon “MuRIL is a starting point of what we believe can be the next big evolution for Indian language understanding. We hope it will prove to be a better foundation for researchers, startups, students, and anyone else interested in building Indian language technologies” said Partha Talukdar, Research Scientist, Google Research India. What is MuRIL? MuRIL, short for Multilingual Representations for Indian Languages, is none other than a free and open-source […]

June 25, 2021 Advanced, NLP, Programming, Project, Python, Supervised, Technique, Text, Use Cases

TS-SS similarity for Answer Retrieval from Document in Python

This article was published as a part of the Data Science Blogathon Introduction This article focuses on answer retrieval from a document by using a similarity algorithm. This task falls under Natural Language Processing which is a subset of Deep Learning. In this article, we will be understanding why do we require better techniques and what are the drawbacks of using naive algorithms. Moreover, we will be implementing a similarity-based technique for answer retrieval from the document. This article is a […]

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