A Brief Overview: What is Natural Language Processing?

With the rise of AI, terms like natural language processing (NLP) are becoming ever more popular.

• Have you ever wondered what’s happening behind the scenes to allow computers to “process” natural language and what that process looks like? 
• How does NLP help computers communicate with us?
• How does it all come together to form sentences and phrases in a coherent way?

Get insight into natural language processing, what it involves, and what innovations and practices researchers are focusing on as artificial intelligence continues to evolve.

The First Step: Text Pre-Processing

The raw text data that’s given to a computer may come with a lot of extraneous code, formatting, and other “leftovers” that make it hard for a machine to understand. Pre-processing converts the text into a clean, easily understood format. 

Examples may also include cleaning or removing HTML tags, scripts, or even ads that are present in online text, as noted by a study measuring the efficacy of text pre-processing (available through Science Direct).

Tokenization

Computers don’t understand sentences and paragraphs the way we do. All of those different lines and words of different lengths and syllables can cause confusion, which is why tokenization is necessary. 

Tokenization breaks down text into individual sections or tokens so that the machine can analyze them independently.

How the tokens are defined or created can vary. In their guide on Tokenization in NLP, Coursera notes that tokens may include:

• Words
• Specific characters
• Phrases
• Sentences

POS tagging

The Encyclopedia of Machine Learning notes that POS or part-of-speech tagging assigns the parts of speech (think nouns, verbs, and adjectives) to each word in a sentence. 

This helps machines understand what category the word falls into as it works to understand how these words relate to each other in a sentence. POS tagging also helps give words context. 

For example, “run” in English can be used as a noun (I went for a run) or a verb (I run every day). POS tagging makes the context of these words clearer. 

Named entity recognition

Another component involves NER or Named Entity Recognition. A paper available through MIT Press Direct defines NER as the ‘task’ which categorizes a word as ‘person, location, or organization name.’

This helps the system recognize the difference, for instance, between Apple, the company, and an apple, the fruit. 

It also helps the system to understand that while both of these are nouns, one refers to a brand, and the other refers to food. 

A Closer Look at Syntax and Semantics

Even after breaking the text down into smaller chunks and categorizing nouns, verbs, and so on, the work still isn’t done.

The machine also has to learn syntax and semantics. The syntax is simply how words are arranged in a sentence to make sense. 

Santa Clara University notes that semantics involves the machine understanding words within sentences. Semantic processing enables the model to determine the meaning of a phrase by using the words that surround it to provide additional context.

NLP Then Generates Language 

Following text pre-processing and semantic processing, the next step in NLP is generating language. 

This part is what many who use AI tools may be most familiar with. It’s where a user inputs  a query and based on its training, the tool generates language or text in response.

How is NLP Used Every Day? 

Now that you understand what natural language processing is, the next question that arises is, how is NLP actually used? Here are a few ways this breakthrough technology is being used right now, in our everyday lives. 

As part of Google searches

You may not realize it, but when you search Google and other search engines, NLP is used to understand the meaning behind your search. If you were to search, for example, “How to fix a cracked phone screen,” NLP understands you want instruction, not just information. 

Google isn’t the only search engine that incorporates AI, either. Check out our guide on AI Search Engines.

Chatbots and virtual assistants

Natural language processing is also used to power AI chatbots and AI virtual assistants. 

A practical application of this is in customer service. A chatbot might try to solve a customer query by way of a knowledge base, tutorial, or FAQ article. Then, it could also forward the request to a human customer service agent, if its initial responses were unable to solve the customer’s questions or concerns.

Streaming content recommendations

Whenever you watch Netflix or other popular streaming services, machine learning and natural language processing may be involved in recommending content based on your viewing history. 

New advances in healthcare

NLP and machine learning are becoming more integrated into healthcare industries. For instance, Yale School of Medicine recently shared details about a new book that’s being published on Natural Language Processing in Biomedicine. The aim of the publication is to provide insights on how NLP can improve the analysis of clinical text and potential applications in the biomedical field.

Current Challenges with Natural Language Processing 

Despite its advances, NLP isn’t without challenges and issues.

• Data privacy is a major consideration.
• NLP also requires large amounts of data as part of its training; sources of data will need to be considered carefully as the technology continues to develop.
• Monitoring data closely is another consideration to avoid training AI models on any inherent biases within the data. 

What’s Ahead for the Future of NLP?

So, where do we go from here? Research is currently ongoing and cutting-edge frontiers are pushing the boundaries of what we know is possible with NLP and AI as a whole. These include: 

• Explainable AI (XAI): NLP is becoming increasingly complex, and with that complexity comes the need to understand why a model made a certain decision. This creates the need for “explainability.” According to Carnegie Mellon University, Explainable AI is research focused on the “set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.”
• Responsible AI Practices: With AI advancements, the need for an established framework that outlines responsible uses of AI is essential. Stanford University offers a fantastic guide on safe, responsible AI practices at the university.

Natural language processing isn’t just a “one and done” process. It’s constantly evolving and incredibly dynamic, pulling together computer science, linguistics, and AI to make human-to-computer interactions feel and seem more natural and intuitive. 

Final Thoughts

Expect AI to continue to make incredible leaps forward as training data and raw processing power become more available.

As a best practice, as AI advances and integrates into a range of industries, maintain transparency around how AI is incorporated into your workflow to keep everyone on the same page.

Looking to learn more about AI detection in this age of AI and NLP? Get insight into AI detection accuracy and read a collection of studies reviewing the efficacy of AI detection.