Articles About Natural Language Processing

Pre-Processing of Text Data in NLP

This article was published as a part of the Data Science Blogathon Introduction In today’s life, a large amount of raw data is available in every sector in the form of text, audio, videos, etc. This data can be used to analyze a wide range of factors which can be used further to make some decisions or predictions. But for this, the raw data has to organize or summarized for getting better outcomes. Here comes the role of NLP, which is […]

Read more

Rule-Based Sentiment Analysis in Python

This article was published as a part of the Data Science Blogathon     Image by Author made online on befunky.com Intro: According to experts, 80% of the world’s existing data is in the form of unstructured data(images, videos, text, etc). This data could be generated by Social media tweets/posts, call transcripts, survey or interview reviews, text across blogs, forums, news, etc. It is humanly impossible to read all the text across the web and find patterns. Yet, there is definitely […]

Read more

Must Know Data Pre-processing Techniques for Natural Language Processing!

This article was published as a part of the Data Science Blogathon Introduction Data from the internet forms a huge source of information these days. We have an overwhelming amount of data available, which includes text, audio, and videos. Text information forms a major source of information amongst these. Natural language processing includes the task of analyzing, modifying, and deriving conclusions from text data. These text or speech data are completely unstructured and messy. A great amount of effort is required […]

Read more

Can Python understand human feelings through words? – A brief intro to NLP and VADER Sentiment Analysis

This article was published as a part of the Data Science Blogathon Introduction Imagine having the power to observe your customer’s thoughts, like what they really think of a particular product/service. For instance, there is a new product launched by NIKE and REEBOK. Both the companies launched a pair of new sports shoes and posted them on their social media accounts like Instagram or Facebook for marketing purposes. Is it possible for an individual to check all the thousands or lakhs […]

Read more

Issue #135 – Recovering Low-Frequency Words in Non-Autoregressive NMT

17 Jun21 Issue #135 – Recovering Low-Frequency Words in Non-Autoregressive NMT Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Non-Autoregressive Translation (NAT), in which the target words are generated independently, is raising a lot of interest because of its efficiency. However, the assumption that target words are independent of each other leads to errors which affect translation quality. In this post we take a look at a paper by Ding et al. (2021) which confirms findings that […]

Read more

Part- 3: Step by Step Guide to Master Natural Language Processing (NLP) in Python

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 part-1and  part-2 of this blog series, we complete the theoretical concepts related to NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts. In this article, we will understand the terminologies required and then we start our journey towards text cleaning and preprocessing, which is […]

Read more

Must Known Techniques for text preprocessing in NLP

This article was published as a part of the Data Science Blogathon In any Machine learning task, cleaning or preprocessing the data is as important as model building. Text data is one of the most unstructured forms of available data and when comes to deal with Human language then it’s too complex. Have you ever wondered how Alexa, Siri, Google assistant can understand, process, and respond in Human language. NLP is a technology that works behind it where before any response […]

Read more

3 Painful Mistakes Leaders Can Avoid When Buying AI Solutions

85% of global executives believe that AI can become their competitive advantage. So, the rush to AI adoption is understandable. Unfortunately, implementing AI from scratch takes time, and success comes with experience in building and deploying solutions. To speed things up, “buying” instead of building from scratch seems like a sensible way to get started; You don’t have to hire a team of data scientists, spend on additional infrastructure, or have support staff on call to troubleshoot model problems. Plus, […]

Read more

Language Translation with Transformer In Python!

This article was published as a part of the Data Science Blogathon Introduction Natural Language Processing (NLP) is a field at the convergence of artificial intelligence, and linguistics. The aim is to make the computers understand real-world language or natural language so that they can perform tasks like Question Answering, Language Translation, and many more. NLP has lots of applications in different fields. 1. NLP enables the recognition and prediction of diseases based on electronic health records. 2. It is used […]

Read more

Develop a Customer Review Analysis Platform from scratch

This article was published as a part of the Data Science Blogathon Introduction When we go to buy anything, what is the one factor that helps us choosing one thing over another? Isn’t it the reviews of that product or service, which represent the brand value? In the era of digital advancement and e-commence, almost every product or service has an indirect or direct digital presence. Consumers of these products and services leave feedback on these over various mediums which creates […]

Read more
1 17 18 19 20 21 71