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What Is NLP and How Does It Affect Your Daily Life Introduction Natural Language Processing, or NLP for short, sounds like something that only computer scientists or AI researchers deal with. But the truth is, NLP is quietly working behind the scenes in your everyday life—often without you even realizing it. From the moment you unlock your phone to the time you scroll through your emails or ask a voice assistant to play your favorite song, NLP is there, helping machines understand and respond to human language. In this blog, we’ll break down what NLP really is, in simple terms, and show you some everyday situations where it’s making your life easier, faster, and sometimes even smarter—without needing you to be a tech expert. What is NLP in simple words Natural Language Processing is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language. Unlike programming languages, which follow strict rules, human language is messy, emotional...

challenges and the use cases of NLP In our previous articles we went over the various approaches to NLP. However, NLP is not without its complexities. In this article, we'll understand why there are many challenges in implementing mainstream NLP in multiple scenarios. We will look at some of the key reasons.  Challenges with NLP: The first one is overfitting on small dataset. Deep learning models have more parameters than traditional machine learning models. This enables the model to fine-tune results. However, this comes with the curse of needing a sufficiently large amount of training datasets. With a smaller dataset, deep learning models tend to overfit, and this leads to low generalization, which ultimately leads to degraded performance and production. There's an interesting concept of Occam's razor, which suggests that, given all other conditions equal, it is always preferable to choose the simplest solution. So for use cases where we have less data initially, it is ad...

machine learning based NLP  In our last article we discussed the heuristic-based approach to NLP . In this article we will understand how machine learning is playing a key role on solving NLP challenges. Like all machine learning problems are applied for other data problems, NLP has a similar approach. On a broad level, we can breakdown machine learning-based NLP task into following categories. Supervised and unsupervised learning in NLP: First is supervised machine learning . Supervised learning enables a model to learn based on historical data. Based on this learning, the model produces an output based on data it has not seen. Machine learning techniques such as classification and regression are also used for NLP. An example of supervised learning is when an ML model can learn the classification of news topic based on analyzing the text. Next, let's talk about unsupervised machine learning . Unsupervised learning enables a model to analyze and cluster unlabeled data. Unsupervised...

Heuristics-based NLP In this article we'd start to understand how the implementation of NLP has been solved gradually. The three ways to solve NLP are heuristic, machine learning and deep learning-based NLP. We will start by understanding how we can solve NLP with a heuristic-based approach. Before we head to the NLP aspect of problem solving, let us try to understand an effective approach to problem solving. Understanding Problem-Solving in NLP: Then we'll be able to chart out a path to solve database problems more effectively. There are fundamentally three steps to consider while solving a database problem. The first is understanding existing rules. While solving problems, it is essential that we understand the fundamentals. Most tasks that we are trying to automate or do better with the help of data are already being done in a certain way. Understanding the existing rules and validating it with experts gives us a second good foundation to build on top. This leads to good fun...

Syntactic and semantic analysis for NLP In this article, we'll understand the two ways to interpret language; one is syntactic and the other is semantic analysis. Languages can be complex and can be understood if the right method of analysis is used. The two methods of analysis that leads to the understanding of languages are as follows, the first being syntactic analysis. Syntax represents the grammatical structure of a sentence. It provides the logical meaning of sentence. Logical meaning in language is brought about by following the standard rules of grammar. Syntactic analysis involves the process of analyzing natural language with the rules of formal grammar. This is also called as parse and syntax analysis. There are primarily two types. One is down parsing and the other is bottom-up parsing. Both are different parsing techniques and strategies used to understand languages. Next, let's talk about semantic analysis. While syntax looks at grammar, "semantic" is re...

Natural Language processing In this article, we will explore and understand what is natural language processing and how is NLP helpful in the real world. NLP is used in the world all around us. A good way to gain a deeper understanding is by exploring the problems that NLP solves and then categorizing their task in NLP. Let's start by understanding some of the fundamentals of NLP. Natural language processing is an area of computer science and artificial intelligence. That involves the study of human language through algorithms. It's a combination of computational linguistic, machine learning, and deep learning models. The goal of NLP is to 'understand' language's whole meaning along with the complete intent and sentiment. Some popular applications are Amazon Alexa, Apple voice assistant, Siri, and tasks such as machine translation you have seen in Google Translate. Let's explore some of the problems solved by NLP. NLP has helped us solve some real-world problem....