Whether we’re aware of it or not, semantics is something we all use in our daily lives. It involves grasping the meaning of words, expressing emotions, and resolving ambiguous statements others make. For example, when your professor says your contributions to today’s discussion were “interesting,” you may wonder whether she was complimenting your input or implying that it needed improvement (hopefully the former). 

It makes sense, then, that different forms of AI, like chatbots and virtual assistants, could also benefit from using semantics. After all, if it helps us, it may also aid them in understanding what people are asking so they’re able to provide the most accurate answers. That’s where natural language processing (NLP) comes in. But what is NLP? 

Today, we’re breaking down the concepts of semantics and NLP and elaborating on some of the semantics techniques that natural language processing incorporates across various AI formats. 

What Are Semantics? 

In the most basic sense, semantics refers to the study of words. To dig a little deeper, semantics scholars analyze the relationship between words and their intended meanings within a given context.  

Take the phrase we used earlier: “dig a little deeper.” In a literal sense, it refers to digging farther into the ground. But we know that in this instance it’s being used as an idiomatic expression. In this context, the phrase carries the figurative meaning of going beyond the surface level to find an answer. 

With semantics on our side, we can more easily interpret the meaning of words and sentences to find the most logical meaning—and respond accordingly. 

How Does Semantics Fit into Natural Language Processing? 

Picture yourself asking a question to the chatbot on your favorite streaming platform. Since computers don’t think as humans do, how is the chatbot able to use semantics to convey the meaning of your words? Enter natural language processing, a branch of computer science that enables computers to understand spoken words and text more like humans do. 

NLP uses different types of computational linguistics modeling, including: 

  • Statistical 
  • Machine-learning 
  • Deep learning 

The more examples of sentences and phrases NLP-driven programs see, the better they become at understanding the meaning behind the words. Below, we examine some of the various techniques NLP uses to better understand the semantics behind the words an AI is processing—and what’s actually being said. 

Word Sense Disambiguation 

As we mentioned above, words often have more than one meaning. With word sense disambiguation, computers can figure out the correct meaning of a word or phrase in a sentence. For example, the word “bear” has two meanings. It could reference a large furry mammal, or it might mean to carry the weight of something. NLP uses semantics to determine the proper meaning of the word in the context of the sentence. 

Sentiment Analysis 

Words can carry either positive or negative emotional connotations. Let’s look at the word “bold” as an example. When we say, “Your style is so bold and confident,” it has a positive meaning. However, the statement, “It was bold of you to assume we liked that type of style” has a more negative meaning. NLP-driven programs that use sentiment analysis can recognize and understand the emotional meanings of different words and phrases so that the AI can respond accordingly. 

Information Retrieval 

One of the main reasons people use virtual assistants and chatbots is to find answers to their questions. Question-answering systems use semantics to understand what a question is asking so that they can retrieve and relay the correct information. 

Natural Language Understanding 

Natural language understanding (NLU) allows computers to understand human language similarly to the way we do. Unlike NLP, which breaks down language into a machine-readable format, NLU helps machines understand the human language better by using  semantics to comprehend the meaning of sentences. In essence, it equates to teaching computers to interpret what humans say so they can understand the full meaning and respond appropriately. 

Earn Your BA or MA in English at The University of Texas Permian Basin 

Sure, you use semantics subconsciously throughout the day, but with an English degree, you can dive deeper into the world of words to analyze word and sentence meaning, ambiguity, synonymy, antonymy, and more. If the idea of becoming a linguist or computational linguist (someone who works at the intersection of linguistics and computer science) piques your interest, consider earning your BA or MA in English at UTPB. 

Both of our programs are entirely online and offer an array of benefits: 

  • Affordable 
    Save with our widely acknowledged low tuition rates. 
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    Small class sizes allow for one-on-one attention from our renowned faculty. 
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  • Accelerated  
    Finish your degree in as little as a year and a half. 
  • Flexible  
    Build your curriculum to match your interests and choose from four different capstone course types. 

Of course, you don’t need to be an aspiring linguist to be the perfect fit for our program. Graduates with degrees in English have plenty of career opportunities: 

  • Journalist 
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Take the first step to achieving the career of your dreams and apply today! 

Sources: 
https://lhncbc.nlm.nih.gov/ii/areas/word-sense-disambiguation.html
https://www.ibm.com/topics/natural-language-processing
https://monkeylearn.com/sentiment-analysis/