Take a moment to think about our lives before the advent of auto-correction features or apps providing online translations.
Sounds even unimaginable!
The contribution to making our lives easier with different efficient applications has been made by the NLP (Natural Language Processing), a field of artificial intelligence.
Businesses use NLP to drive smart decisions for their projects. Adopting its use to understand what’s relevant, gain valuable insights, and then reach out to ato get your desired app developed finally seems like a perfect plan.
In this article, we would be covering the applications that can be considered the best use of NLP.
But first, let’s develop a better understanding of Natural Language Processing.
What is Natural Language Processing?
NLP (Natural Language Processing) can be considered the subfield of AI (Artificial Intelligence), a link between computers and human language.
It interprets the interaction between the computer and the human language. It involves reading, understanding, and making sense of human language in a valuable manner.
And in the case of mobile application development, NLP allows developing apps more smartly and efficiently by analyzing whatever user means.
The businesses use NLP to derive detailed information about their users’ intent and provide solutions.
Mobile Applications Using NLP
Since Natural Language Processing enables gathering insights about users along with earning a competitive lead, here are the top applications already making use of NLP:
Text prediction can be referred to as predicting which word would come next or fits best in a particular sentence or phrase.
Google search is one great example of text prediction!
The NLP algorithm used by Google is known as BERT (Bidirectional Encoder Representations from Transformers).
Various other applications such as Gmail Compose, Google Docs, etc. also use the NLP technology to help the purpose of text prediction.
Sentiment analysis is referred to investigating what emotions are being expressed in a particular data.
It allows businesses in examining what a customer feels about their brand, products/services, etc.
The use of NLP in sentiment analysis has exceptional offerings. Market research, product analysis, public relations, brand management, and targeting are few areas where sentiment analysis serves to a remarkable extent.
Machine Translation is about the use of applications that translate the text to speech and vice-versa.
There are many translators available online; one great example is the Google Translator, which uses neural machine translation to translate a word, phrase, or sentence in no time. It makes use of the machine translation approach where different examples are used to generate an apt output.
However, the human language in the form of words, sentences could be challenging to understand at times.
This is where NLP comes into play to win over such challenges. You can reach out to a mobile app development agency to guide and assist you with the use of NLP in Machine Translation.
One best, in our opinion, is Hestabit. It is the agency that does your work as their own work with utter dedication and offers desired results.
Data in its unstructured form is available everywhere. The NLP text classification can be used to have help with social media, email, websites, etc.
For instance, the categorization of emails in different categories is an example of NLP text classification.
Other areas using the NLP text classification to automate text include discussion forums, news agencies, eCommerce, content generators, etc.
Process of OCR
OCR (Optical Character Recognition) is used for digitizing the printed text, i.e., the images in the written form get converted to machine-encoded language. It helps to perform further editing, saving, or searching online.
NLP with Optical Character Recognition provides a bunch of benefits, including filling data into forms, automated identification of IDs or passports, customer data verification, scanning bank detail, tickets, etc.
Natural Language Processing, becoming the primary reason behind the Voice User Interface, has marked a matchless use in the field of speech recognition.
It introduced the voice to provide the system with the inputs. Siri, Alexa, Google Assistant, and Cortana are the finest examples of speech recognition using NLP.
The speech-to-text feature that we see in smartphones is another prevalent example of speech recognition. It allows the users to provide input in the form of voice or audio to get the desired text output. Google voice search fits well in this category.
A spell checker can be considered as a software that not only identifies the spelling errors, typos in a particular text, or data but also corrects them.
Grammarly is one of the most famous and best examples of a spell checker that checks, suggests, and fixes all sorts of text errors as the user continues to write in a document.
Spell Checkers are also a part of website searches where users are highly prone to making spelling or text errors, which further affects the output of results.
The use of NLP in spell checkers avoids the possibility of making typos in the text.
The Natural Language Processing proves to be helpful in detecting the urgent requirements in the text.
Once an urgency detection model is thoroughly and successfully trained, it can identify the content that is not appropriate in a particular context. It would enable you to prioritize your data accordingly, along with improving the time and efficiency taken to respond.
Natural Language Processing has managed to spread its wings in the different segments with all its offerings.
The mobile applications are turning into the future of the mobile industry. And one can develop countless exciting applications by making use of NLP. The need is just to hire the right mobile app development agency such asand get on board with the development of your project with the integration of Natural Language Processing.
You never know what great results you would end up with!
Structuring the unstructured data, automating the tasks that consume your precious time, and gaining insights for decision-making makes for the fantastic use of Natural Language Processing.
With the integration of NLP for the development of features enriched mobile apps, you never know what great results you would end up with!