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Important Use Cases of NLP

Ananya Avasthi
October 8, 2021

Important Use Cases of NLP

What is Natural Language Processing (NLP)? Natural Language Processing is a subset of Artificial Intelligence (AI)  that assists the system 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 and exceptions. A computer can not naturally understand the nuances of grammar and different meanings: This is where NLP comes into play. 

In today’s day and age, NLP is used everywhere, and some general applications of NLP are:

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 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 (for instance, Supervised learning, Recurrent neural networks (RNN), or Latent semantic analysis (LSA)) to eventually be able to predict where the sentence is heading to. 

Conversational AI

Conversational AI can communicate through voice. Conversational AI is usually deployed as a voice assistant.  Siri, Cortana, 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. 

To complete a task, ASR works by:

-It divides the audio of a speech recording into individual sounds (tokens) and analyzes each sound.

-It uses algorithms (NLP, deep learning, Hidden Markov Model, N-grams) to find the most appropriate word fit in that language.

-ASR converts the sounds into text.

Automatic text summarization

Automatic text summarization involves shortening long messages or sections and creating a unique paragraph that summarizes the message. There are two fundamental techniques, to sum up, texts: Extractive outline: In this strategy, the output message will be a mix of significant sentences removed straightforwardly from the first message. 

Abstractive Summary: 
This strategy is further developed, as the yield is another text. The point is to comprehend the overall significance of sentences, decipher the specific situation, and create new sentences dependent on the general importance. 

In the two strategies, NLP is utilized in the text translation steps, which are: 

-Cleaning the text from filling words

-Testing the message into more limited sentences (tokens) Making a comparability network that addresses relations between various tokens 

-Working out sentence positions dependent on the 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.

Conclusion

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.