Last Updated on January 21, 2022
Once in a blue moon when we someone without a mobile device in this 21st century. The number of mobile users is increasing rapidly at the worldwide level. With innovative technology, AI-powered human-to-machine interactions are a dime in a dozen. Data science and machine learning technology have improved public organization and business activities.
Siri, Cortana, and Alexa are a few popular examples of virtual assistants. They make our lives very relaxed. Let’s say Alexa or it could be any virtual assistant, how are they clever to do it? It is all possible with natural language processing.
Let us introduce to you the term swiftest developing AI technologies Natural Language Processing (NLP) throughout this article. A brief breakdown of NLP tasks performed by NLP software we‘ll cover in a bit.
What Is Natural Language Processing?
This technology has been everywhere over the years and uninterruptedly enhanced the activities in trades exclusively in the business activities. Natural language processing is AI technology that recognizes and understands natural human languages. The amalgamation of artificial intelligence and computational dialectology. In particular in what way to program computers to process and analyze big quantities of natural language data.
It improved the mode of communication via speech, text, virtual conversation, and messaging. NLP techniques make computers comprehend the converted form of written or spoken human speech. translators, voice assistants, Spell-checkers, online search are all the function where Natural language processing technique is used.
How Does NLP Work?
Written and spoken human language is altered into an adequate and understandable mode for computer form that is natural language processing techniques. The technique used is believed effective and appreciated for businesses.
All NLP methods are similar whether there is a chat with a chatbot or processing an automatic translation. A similar method of all NLP follows understanding the hierarchies that command interaction amid distinct words. It is not as it seems the problem arises when the same word has a double meaning with the sentence. In this scenario, the difference is to understand by computers with the whole sentence.
The unstructured language data is transformed into an understandable computer language by NLP. For this NLP applies algorithms to identity and extracts natural language rules. After receiving the text data, the computer uses algorithms to abstract its meaning and collect the important data from them.
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two main aspects of NLP. NLU is where the computer assigns the meaning of language received by it. NLG is where the process changes the data gathered from the computer’s language to human-understandable language.
NLP instances are spell checkers, online search, translators, voice assistants, spam filters, autocorrect, NLP business applications are used so commonly these days in dissimilar systems.
What Is NLP Used For?
- NLP is commonly used in language translation applications for example as Google Translate, Microsoft Translator, iTranslate Translator.
- Siri, Cortana, Alexa, and Ok Google are all virtual personal assistants that use Natural language processing.
- To respond to individual customers perfectly while solving their queries Interactive Voice Response apps are used in call centers.
- Chatbots responding to individuals.
- Spam filters are used to remove unwanted emails and differentiating non-spam from spam emails by extracting the meaning and regularity of certain words detected in the email section.
- It handles people’s feelings about definite topics or services with sentiment analysis.
Brief Breakdown Of NLP Tasks Performed By NLP Software
Summarization is the process that comprises text shortening by classifying the significant parts and making a summary. In summarization two approaches are followed for creating a summary.
Abstractive creates a new sentence that was not present earlier. The afresh generated sentence can be or cannot be present in the original text.
The sentence summary is extracted from the provided sentence. Identification of the significant sentences or it could be phrases from the original text and removing them from the text.
2. Language Modelling
Language modeling is referred is when NLP performed a task that consists of predicting the subsequent word, a character in a text, document. There are various uses of language modeling let’s check it out.
- For summarization of text, document it is used.
- For recognizing the handwriting, it is used.
- For captioning the image.
- For optical Character Recognition Machine Translation.
- For correcting the spelling with autocorrect.
3. Named Entity Recognition
Named entity recognition is the process that indicates identification entities such as person, organization, date, location, time in a sentence. After this, the classification is made into categories for better understanding.
4. Text Classification
Text classification encompasses assigning categories to text conferring to the content. to structure, organize, and categorize any text classification is used. Text classification takes in the user interface which is quite straightforward and easy to use. The text classifier then takes the input of the text, analyzes its content. Afterward, automatically assign appropriate tags to it.
5. Sentiment Analysis
Sentiment analysis is the process that consists to identify positive or negative feelings in a sentence, the sentiment of a customer evaluation, judgment of attitude through written text or voice analysis for a comprehensive range of subjective analysis.
6. Part Of Speech Tagging
Part of speech tagging is the process that consists of tagging and marking up words in a sentence as nouns, verbs, adjectives, adverbs, and other descriptors.
How To Use Natural Language Processing In Mobile Apps?
NLP is the technology that is improving mobile app devices with innovations. Developers are making constant efforts for mobile app development with artificial intelligence technology. Mobile app development using machine language that derives with progressive explanations for business.
Let’s grasp numerous kinds of mobile applications that practice NLP technology in diverse subdivisions like a search engine, protection from spam, in the medical field.
1. NLP To Initiative An Information Search Engine
A virtual assistant will deliver better results. NLP-based technology in mobile devices to provide start in-depth explanations to the user’s complex queries. an information engine that uses websites, videos, eBooks, data stores, videos, and television material. virtual assistants such as Siri, Cortana, and Alexa are the finest at providing a basic answer to a simple question.
2. NLP For Mobile Application For Protection From Spams
When it comes to monitoring spam messages NLP works effectively. NLP technology can read and understand the content of comments on the blog, email text, private posts on social media platforms, and more. Content is compared to recognized spam messages to classify the spam.
3. NLP For Mobile Apps In Medical Field
NLP technology robotically fills out a well-being history procedure of patients by only using an app while talking to patients. NLP technology can help patients take notes derived directly from the doctor’s speech. Medication names, dosage information, and other tips are all crucial information received from NLP to patients. Surprisingly this kind of mobile apps is used to send well-being updates to the family of the patients.
Let us make a long story short natural language processing so far so good is an artificial intelligence technology that can have an extraordinary influence on mobile app development at a worldwide level. Python applications have also contributed to developing NLP apps. Mobile devices and numerous gadgets are becoming smarter than ever before as the technology is supported by NLP. Significantly it providing help to businesses with flourishing and enhancing the customer experience while maximizing output for every industrial company. There’s no aim to presumption, but we can securely say that it’s been used and the users are constantly increasing with the mounting artificial intelligence trends.