Getting Started with Natural Language Processing – Fundamentals of Natural Language Processing
Getting Started with Natural Language Processing
In this section, you will learn about natural language processing fundamentals such as what it is, business applications of NLP, how it works, stages of natural language processing, and core NLP responsibilities.
What Is Natural Language Processing?
Natural language processing (NLP) is a type of artificial intelligence (AI) that lets computers understand both written and spoken human language. It was made so that software could be made that creates and understands natural language. This way, a person could talk to a computer in a natural way instead of using programming or artificial languages like Java or C.
It is the practice of using computer techniques and artificial intelligence to allow computers to identify and respond to human speech. There are various approaches to NLP, but they all involve segmenting a speech or text into its component parts and correlating those parts to a library that shows how those parts fit together based on previous events. Text-to-speech apps, which are now available on most iOS and Android devices, as well as smart speakers like the Amazon Echo (Alexa) or Google Home, are good examples of NLP.
Natural language processing (NLP) is a kind of machine learning that enables computers to decipher, modify, and understand natural speech. The amount of voice and text data that businesses produce today through email, text messages, social media news feeds, video, audio, and other forms of communication is enormous. Using NLP workloads, they decipher this information on demand, figure out what the message is trying to say or how people feel about it, and respond to human dialogue in real time.
Natural language processing (NLP) is required for in-depth analysis of text and verbal data. To prepare data for use in various applications, NLP software employs preprocessing techniques like tokenization, stemming, and stop word removal.
It can work around language differences, slang, and weird grammar that happen in everyday conversation. Businesses use it to automate a wide range of tasks, including
- Text file inspection for important terms and recognition of entities
- Carrying out sentimental examination to assume the negativity or positivity in used language
- Translation of noted down or spoken words intelligently across languages
- Deciphering given directions and deciding on the best course of action for the given task
NLP can also be used to improve customer communication in client applications. For example, a chatbot looks at customer questions and sorts them by how common they are. It automatically answers the most common questions and sends the more complicated ones to customer service. This automation lowers charges, saves agents hours on repetitive questions, and boosts customer satisfaction.
