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Natural Language Processing, Editorial, Programming
Understanding Semantic Analysis Using Python — NLP
How do machines understand our language? This tutorial dives into semantic analysis, a crucial area of natural language processing (NLP)
Author(s): Daksh Trehan, Roberto Iriondo
We live in a world that is becoming increasingly dependent on machines. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly). But how do they do that? Today we will be exploring how some of the latest developments in NLP (Natural Language Processing) can make it easier for us to process and analyze text.
Can computers understand and respond to human language? One of the most fundamental questions in Computer Science. Humans have discussed this subject for many centuries: can machines “think,” do they “feel,” how close are we to creating a thinking machine?
For many of us, machines and computers are a mystery. They do what we program them to do, but they constantly learn and adapt (which is scary in some cases). One of the fascinating things about computers and artificial intelligence is how they can understand human language. How did they get so bright? For that matter, what makes our language so intelligent that it can convey so much meaning in so few words? Understanding these concepts is critical if we want seamless communication between humans and computers. This article will outline how semantic analysis works and outline the basics of Python for building NLP-related systems using one of the most essential NLP techniques: semantic analysis.
NLP, or natural language processing, has been around for decades. It is fascinating as a developer to see how machines can take many words and turn them into meaningful data. That takes something we use daily, language, and turns it into something that can be used for many purposes. Let us look at some examples of what this…
