NLP is a growing field with an incredible number of real-world applications. So what is natural language processing (NLP) and how does it relate to AI? NLP is a subset of artificial intelligence (AI) that assists systems in understanding natural language or human language. Computers understand programming languages that are very straightforward and do not have exceptions. Human languages are diverse, with hidden meanings, exceptions, and subjectivity. A computer cannot naturally understand the nuances of grammar and different meanings, and this is where NLP comes into play.
In today’s day and age, NLP is used everywhere. Let's take a look at some of the applications of NLP in our world.
Translation is one of the top—and original—use cases of NLP. The primary NLP-based interpretation machine was introduced during the 1950s by Georgetown and IBM, which could consequently translate 60 Russian sentences to English. Today, translation applications influence NLP and AI to comprehend and precisely translate worldwide dialects in both text and voice designs.
Stakeholders utilize NLP to recognize incorrectly spelled words. They do this by cross-matching words to an appropriate word set via a language dictionary using the word reference used as a preparation set. The incorrectly spelled word is then put through the machine learning algorithm that addresses words in the preparation set, adds, eliminates, or replaces letters from the word. It also matches the words to a batch that fits the general importance of a sentence.
Autocomplete uses NLP with specific machine learning algorithms such as supervised learning, recurrent neural networks (RNN), or latent semantic analysis (LSA) to predict where the sentence is going.
Conversational AI can communicate through human language. Conversational AI is usually deployed as a voice assistant like Siri, Cortana, and Google Home. Another example of a conversational AI is a voice layer on a website or a virtual call center agent.
Voice recognition, also called automatic speech recognition (ASR) and speech to text (STT), is a kind of algorithm that converts human speech from its analog form (acoustic sound waves) to a digital format so that the AI can understand and recognize it (also learn how NLP fuels chatbots).
• Dividing the audio of a speech recording into individual sounds (tokens) and analyzing each sound
• Using algorithms (NLP, deep learning, Hidden Markov Model, N-grams) to find the most appropriate word fit in that language
• ASR converting the sounds into text
Automatic text summarization involves shortening long messages or sections and creating a unique, summarizing paragraph. There are two fundamental techniques:
1. Extractive outline: The output message will be a mix of significant sentences removed straightforwardly from the first message.
2. Abstractive summary: The point is to comprehend the overall significance of sentences, decipher the specific situation, and create new sentences based on the general importance.
For both techniques, NLP is utilized in the text translation steps, which are:
• Cleaning the text from filling words
• Testing the message with more limited sentences (tokens)
• Making a comparability network that addresses relationships between tokens
• Working out sentence positions based on semantic likeness
• Choosing sentences with the highest levels to create the outline (either extractive or abstractive)
Language models are AI models which depend on NLP to assist in determining how to produce human-like text and discourse. Language models are used for machine interpretation, grammatical form (PoS) labeling, optical character recognition (OCR), penmanship acknowledgment, etc.
A portion of the renowned language models is GPT (Generative Pre-trained Transformer), created by OpenAI and LaMDA by Google. These models were prepared on massive datasets from the web and web sources to computerize undertakings requiring language understanding and technical refinement. For example, GPT-3 is the most talked-about AI—said to mimic writing like a human—also containing the largest neural network ever created to date.
The real-world potential for NLP is staggering. There are so many ways that NLP and data labeling tools can help businesses and people, and this is going to be an ever-evolving list. Here are just a few of the many examples of real-world NLP uses:
Legal firms use NLP labeling tools to review client contracts, track down information relevant to a case, and efficiently search through legal research to label and connect legal documents. The legal industry involves a huge amount of data and complex documentation, and NLP is crucial for combing through and organizing that information to prepare for a case. NLP can be used to translate obscure legalese in contracts into plain language with speed and accuracy. This frees up lawyers’ time to focus on what they do best.
Services like Amazon and Netflix use machine learning algorithms to analyze your activity, correlate it with millions of other users, and then come up with informed recommendations for what else you might like. NLP and data labeling help build the accurate ML models that make this possible, and the recommendations get smarter all the time with new data and ML models. Similarly, in marketing, being able to understand your customers helps you be able to sell to them effectively. That’s the foundation behind marketing personalization, and NLP can play a key role in labeling the customer data to inform marketing efforts.
Fintech involves handling real-time transactions, securely managing assets, fraud detection, and more. NLP can help streamline these Fintech processes in a huge way. For example, NLP and data labeling tools can help companies to recognize intent and direct customer requests, pass claims, improve customer experience, and securely organize databases and documents. NLP assists in turning unstructured data in databases and documents into structured data and extracting relevant insights through pattern recognition (text mining).
NLP has a huge role to play in the healthcare industry. It can be used to help with transcribing doctors’ notes, improving hospital discharge notes, upgrading the patient experience, and more. NLP can help with scanning scientific journals and papers for promising new medical treatments, classifying medical claims, and looking for patterns with patients. As the healthcare industry evolves, so can the way that NLP can help.
NLP has become an integral part of many industries. For example, in the medical industry used for dictation, clinical documentation, clinical trial matching, etc. NLP assists the AI to become a better functioning computer that behaves more efficiently than a human but simultaneously like a human.
If you’d like to talk more about NLP use cases or how NLP labeling could help you, feel free to reach out to info@datasaur.ai. We'd love to chat!
