What Is Natural Language Processing (NLP)?
Chatting with robots, whether by text or by voice, has become a normal part of everyday life for millions of people around the world. As smartphones and other devices become widespread, we have easily adapted to asking our computers to tell us the weather, give us directions, dial a phone number, or look up a restaurant. This is all possible, thanks to natural language processing, or NLP. NLP is the technology that is hard at work inside your smart device every time you ask it a question or give it a command. So what is NLP, and how is it related to artificial intelligence (AI)?
Artificial Intelligence and Natural Language Processing
Artificial intelligence is exactly what it sounds like: a machine’s imitation of human intelligence. Simply put, AI is when a computer is capable of learning how to do something it has not explicitly been programmed to do. The AI allows the machine to learn from previous information or data it has received and patterns that it knows already to make predictions and perform tasks. Natural language processing is simply a type of AI. It is AI that has been directed to understand human language in various forms.
One way of understanding natural language processing is to think of it as what makes it possible for humans to talk to machines. What does this phrase mean? When humans talk to each other, a great deal of information is communicated in addition to the words themselves. Understanding grammar, vocabulary, and syntax is only the foundation for communication. Tone, context and inflection are all key to understanding human speech and writing. Natural language processing aims to teach computers how to understand all of these aspects of human communication through AI.
Natural Language Processing: A Brief History
We often think of AI and NLP as new technologies. In fact, both have been around for many decades in various forms. The origin of NLP dates back to the World War 2, when computers were used for machine translation between English and Russian. It developed under the name computational linguistics. Programmers worked hard to teach computers how to interpret human language. They did this by using a rule-based approach, teaching machines the rules of grammar, spelling, and syntax. This approach is still used to solve some NLP challenges today. However, thanks to AI developments, much of NLP utilizes machine learning and deep learning technologies in addition to this.
When using NLP to solve specific challenges, there are a number of different approaches. One is to build your own model and train it starting from zero to do what you want to do. However, this takes a great deal of time, data, and resource. A more common approach is to use a pre-trained model. This allows you to achieve results much faster and requires a lot less data. The following are three of the top models currently.
GPT-3 is an autoregressive language model with 175 billion parameters that is capable of translation and question-answering, as well as more complex tasks like unscrambling words. While GPT-3 still struggles with some language tasks that people typically do with ease, the model has been able to write short news articles that closely mimic the writing of humans.
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a model trained on a very large corpus. It considers the context on both sides of a word when trying to understand a sentence. BERT has already given rise to some further achievements such as writing summaries and mining biomedical text and is an important baseline for further NLP development.
A recent and exciting development in the world of NLP models, the Jurassic-1 language model can be used to build your own applications through the AI21 Studio developer platform. Jurassic-1 has two model sizes, with its larger version the biggest and most sophisticated language model released thus far for use by developers. Custom models of Jurassic-1 can be trained with very small datasets, and it is exciting what possibilities this new development brings to NLP.
NLP in Our Lives Today
NLP is all around us today. NLP is what has made it possible for you to ask Siri or Bixby to look up information, or tell Google Home to turn your lights off for the night. Natural language processing has made it possible for us to tell Amazon Alexa to order more light bulbs.
However, the influence of NLP is by no means limited to voice-driven AI technologies. We encounter it in chatbots, which are becoming increasingly common as customer service assistants. Sentiment analysis, which is a growing area in marketing and consumer studies among other fields, uses NLP to analyze the tone of online comments and survey responses. This makes it possible to rapidly process a huge amount of data quickly to grasp how consumers are feeling about a product, campaign, or brand.
AI grammar and writing checkers are another area where NLP is driving rapid development and changes. These checkers have come a long way from simple spelling and grammar proofreading tools that are used at the final stage of writing a paper. These days, NLP has made it possible for these tools to suggest tone and style corrections, differentiate between language use in specialized academic and technical fields, and make recommendations for clarity. The number of tools available has proliferated and includes Grammarly, which is a general grammar check tool, Trinka, which is a valuable writing assistant for academics, and ProWritingAid, which helps fiction writers.
Future Trends for NLP
Like AI, NLP is developing quickly as computing power increases and the amount of data available grows. Just this year, there have been some remarkable breakthroughs in NLP technology such as the creation of machine learning models capable of writing articles from scratch. Developments have been made in multilingual NLP as well, with Facebook and Google publishing models that don’t need to rely on English as the intermediate language. This implies that NLP will be able to develop in many languages more rapidly in the near future.
COVID-19 has kept many people at home, which in many countries has led to an increase in online purchases and customer service requests. Many companies have started to incorporate chatbots as a kind of frontline customer service, and this trend seems poised to continue into the next decade as NLP improves.
Finally, the increase in the quality of sentiment analysis is making more companies wonder how they can take advantage of this unique technology. The increase in multilingual NLP mentioned above makes sentiment analysis possible at a scale that was previously unprecedented. Some predict that companies will direct sentiment analysis not only toward their customers, but also to understand how their employees are feeling about their work lives.
NLP is a fascinating and rapidly developing technology that underpins many of the tools making our lives simpler and more efficient today. From customer service to writing help and digital assistants, NLP is all around us. We can expect this to only continue going forward.