Important Use Cases of NLP

NLP has many use cases. In this guide, we discuss some of those uses such as abstractive summary, text summarization, translation, autocorrect, and more. 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.
Post Header Image
Ananya Avasthi
PUBLISHED ON
March 24, 2022
PUBLISHED ON
March 24, 2022
October 8, 2021
Post Detail Image

Important Use Cases of NLP

NLP is a growing field with an incredible number of real-world applications. So what is natural language processing (NLP) and how is 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 can not 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

The translation is one of the top 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.


Autocorrect

Stakeholders utilize NLP to recognize an incorrectly spelled word. They do this by cross-matching it 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 Sentences

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

Conversational AI can communicate through voice. Conversational AI is usually deployed as a voice assistant. Siri, Cortana, and Google Home are a few examples. Another example of a conversational AI is a voice layer on a website or a virtual call center agent.


Voice Recognition

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). 

To complete a task, ASR works by:

• 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

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

Language models are AI models which depend on NLP that assist in determining how to produce human-like text and discourse as an outcome. Language models are utilized 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.


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!

"Most comprehensive labelling tool in the market. Datasaur has saved us countless hours in building our own solution. My team lead never wants to go back to spreadsheets!"

G2 Reviewer

"Operating in an industry where we have to be privacy- and security-conscientious with our data, Datasaur was the only acceptable solution for us. We recommend them for both feature set and support responsiveness."

G2 Reviewer

"...information labeling tasks has been reduced by 80% which has allowed us to optimize our workflow much more, allowing us to focus on other areas that are also priorities for us..."

Mary L