What is Stop word in NLP?

Stop words are the most common words in any language that do not carry any meaning and are usually ignored by NLP. In English, examples of stop words are “a”, “and”, “the” and “of”. In NLP, stop words are typically removed from a text before it is processed for analysis. This is done to reduce…

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What are the Different Applications of NLP in the Real World?

NLP is a branch of artificial intelligence. It is used to analyze and understand human language. NLP has many applications in the real world that can be used for different purposes. Some of these applications include: Natural Language Processing (NLP) is a technique for understanding the sentiment in text. NLP can be utilized to identify…

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Interesting applications of blockchain in ML and NLP?

In recent years, the blockchain has been used to track and store information on a variety of applications. It can be used to store data about transactions, contracts or other records in a secure way. Blockchain is decentralized and not controlled by any single entity. This makes it an ideal technology for machine learning algorithms.…

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How Blockchain can be used in Natural Language Processing?

The blockchain is a revolutionary technology which has the potential to change how we do business. It can be used in many different fields and industries, but one of the most interesting applications is in Natural Language Processing. The blockchain has the potential to revolutionize how data is stored and shared. It can store data…

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What is the future of Natural Language Processing in healthcare?

With the development of natural language processing (NLP) and machine learning, natural language processing in healthcare is becoming more and more important. NLP is a computer programming technique that uses statistical techniques to analyse text or speech and extract meaning from it. It has been used for many years in computational linguistics but only recently…

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How does sentiment analysis work

Sentiment analysis is a natural language processing technique that analyses the sentiment of a text. It is used in many applications, such as opinion mining and customer feedback. Sentiment analysis is an increasingly popular research topic in the field of computer science. It’s a process that entails three major parts: sentiment analysis, sentiment classification, and…

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What are the Challenges of Natural Language Processing

Natural Language Processing is that the field of design methods and algorithms that takes as input or produce as output unstructured. Human language is highly ambiguous (consider the sentence I ate pizza with friends, and compare it to I ate pizza with olives), and also highly variable (the core message of I ate pizza with…

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Stemming vs Lemmatization Difference: Explained in Detail

Introduction When dealing with large amount of text data, it becomes essential to preprocess and analyze the text effectively. Stemming and lemmatization are text processing techniques that help reduce words to their base forms, helps you in better analysis and understanding. Stemming Stemming is a technique that aims to reduce words to their root form,…

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Tokenization in NLP: Breaking Language into Meaningful Words

Tokenization is a fundamental concept in Natural Language Processing (NLP) that involves breaking down text into smaller tokens. Whether you’ve heard of tokenization before or not, this article will help you get the clear and concise explanation. What is Tokenization? Tokenization is the process of dividing a given text, such as a document, paragraph, or…

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Difference between Deep Learning and Natural Language Processing?

In the realm of artificial intelligence, two prominent domains, Deep Learning (DL) and Natural Language Processing (NLP), have emerged as powerful tools. While both are related to machine learning, they serve distinct purposes and possess unique characteristics. This article aims to delve into the dissimilarities between Deep Learning and Natural Language Processing, shedding light on…

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