What Is Natural Language Processing?

Artificial intelligence (AI) is already becoming an integral part of our daily lives, fitting in seamlessly with our day-to-day activities. It has completely changed the way we do most things, particularly when it comes to making our work and communication more efficient.

Even in our personal lives, AI is contributing to every day convenience by offering personalised recommendations on streaming devices or responding to voice commands on smart home devices. 

It continues to evolve at a rapid pace, and so we’ve seen the emergence of another branch of it which is natural language processing (NLP), which gives computers the ability to comprehend, interpret, and then generate human language. 

Essentially, NLP is the technology behind applications such as virtual assistants, chatbots, search engines, and translation tools that we commonly interact with.

 

NLP’s Role In Technology

 

If you think about it, the human language is incredibly complex. Made up of nuances and context, it can be challenging to comprehend at the best of times. It’s completely different to code which is logical and clear-cut, and is often times unstructured and highly ambiguous.

The part that NLP plays is to bridge this gap to help machines adopt a more humanistic approach. Our reliance on technology won’t be slowing down any time soon, so NLP’s role is to create user-friendly experiences that are more natural.

 

A Technical Dive Into NLP Processes

 

In a nutshell, the process of NLP starts with dissecting text into smaller pieces, such as words or sentences. It then uses algorithms to attach meaning to these to be able to understand context and respond accordingly. Let’s get into the step-by-step process.

 

Data Is Preprocessed

 

Before the language can be studied, the data needs to be preprocessed to make it readable to machines. Here, sentences are broken down into each individual word and words that are deemed irrelevant such as “the” or “and” are removed. It will also convert words into their simplest form for example, “walking” would be converted to “walk.”

 

The Linguistic Analysis

 

Once the data has been preprocessed, it can be examined on a much deeper level. This step is made up of two parts:

Syntax parsing: By using dependency parsing, computers can learn how words in one sentence relate to each other to create meaning. It’s almost as if the words are laid out in a tree structure where each ‘branch’ shows how the words are connected. This gives meaning to the sentence as a whole, as opposed to just the individual words.

Semantic analysis: In the English language, one word can sometimes have multiple meanings. Here, words are analysed to understand what they mean based on their context. For example, it will be able to differentiate ‘apple’ the fruit from ‘Apple’ the tech company.

 

How NLP Is Used In Modern Technology

 

NLP has become a dominant feature in most of the modern tech processes, sometimes without us even realising it. Here are a few examples of where you may have interacted with it.

 

Virtual Assistants Or Chatbots

 

When you visit some websites, a chatbot or virtual assistant may pop up. These can answer questions you may have, or help you find something specific. In order to understand your text commands, and then respond accurately, they use NLP to identify what you are asking and what the desired response or action will be.

 

Search Engines And Streaming Recommendations

 

Every time you look for something on a search engine like Google, NLP is used to identify what you are trying to find. Additionally, if you have misspelt a word or used a phrase incorrectly, it will be able to find the correct version.

Similarly, popular streaming platforms such as Netflix offer personalised recommendations for you based on your preferences. This data is made available through the use of NLP to suggest what you will most likely prefer to see.

 

 

Language Translators

 

Apps, such as Google Translate, are powered by NLP and use neural machine translation (NMT) to translate text into various languages. These translations, while sometimes may be slightly off, generally have near-human accuracy.

 

Text And Content Generation

 

AI-powered tools, including OpenAI’s GPT models, have opened the door to endless creative possibilities. They offer assistance with creative writing and even designing images. Developers can even use them for writing code and debugging websites, although 100% accuracy is not always guaranteed.

 

Are There Any Challenges Associated With NLP?

 

NLP has come a long way in its advancements, but it isn’t without a few setbacks. The main ongoing challenge is understanding context. Even the most advanced models can still struggle with comprehending long-form text, which can produce inaccurate responses.

It’s also not uncommon for NLP models to inherit certain biases from the Internet’s data from which they are trained. Maintaining neutrality in these models continue to be an area of development and one that will be focused on improving in the coming years.

 

What Does The Future Hold For NLP?

 

There are already a few predictions regarding NLP that we can expect to see in the near future. Real-time translation will become more prominent, and people will be able to have multi-lingual conversations instantaneously.

Additionally, AI will start to become more emotionally-aware and understand how people feel, not just what they say. In certain industries such as healthcare or customer service, this can support significant advancements.

As Virtual Reality (VR) and Internet of Things (IoT) continue to develop, NLP will make the interactions between humans and machines far more natural and conversational than what it already is.