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Implementing Natural Language Processing (NLP) In Business Applications

In today’s advanced technological world, the use of mobile devices is increasing with every passing day. With this swift growth in usage of mobile devices, more and more mobile app content provides a chance for digging out useful information.

Today, everything is related to voice data and text. It can be social media messages, news feeds, emails, and the free-text format in web pages. To pull out the meaning from such data, various systems have emerged with additional features with our languages. Such systems are based on NLP.

Besides, AI-powered human-to-machine interactions are just a common thing. Businesses and public organizations have been applying machine learning and data science for a while. Today, one of the fastest emerging AI technologies is natural language processing (NLP).

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As per a report, the NLP market will grow to $43.9 billion by 2025.

YEARNLPMARKET (In Billion Dollars)
202543  289.9

Have you ever thought, what’s the reason behind the increasing popularity of NLP every year, and how it’s becoming important for businesses? Such sorts of questions are normal. Today, in this post, we will dive deeper to know about NLP, its working, uses, and more important details about NLP. So, let’s get started.

What Is Natural Language Processing?

An automatic management of a natural language like speech and text by software is known as Natural Language Processing (NLP). This technology is used to help mobile devices or computers to understand the human language. The target of NLP is to read, decipher, and make the sense of the human language profitably. Most of the NLP techniques depend on Machine learning (ML) to dig out the sense from human words.

How Does NLP Work?

NLP applies the algorithms to recognize and extract the natural language rules in such a manner that the unstructured language data is converted into easy computer language that can be understandable. Once the text is offered, the computer uses algorithms to dig out its meaning and accumulate the important data from them.

What Is Natural Language Processing (NLP) Used For?

All over the years, this technology has been used and is constantly improving the quality of people’s lives from all stages of life or the industries especially in the business. An integral part of the computer science of AI, Natural Language Processing (NLP) is a way of communication through text, speech, messaging, and virtual conversion, or making it simply the blend of computational linguistics and artificial intelligence.

This term came into existence in 1956. Since then, AI has been utilized in computer systems to first think and then learn akin to humans. Also, various attempts were used to replicate the human thought processes and the actions within AI apps.

Being a part of Artificial Intelligence, Machine Learning first assisted in revolutionizing Natural Language Processing (NLP) in the late 1980s. Holding Machine Learning, computers used the statistical modes to catch up learning itself by constantly being introduced to distinct or new data with no direct programming.

For years, statistical modeling techniques, like Hidden Markov Models were utilized to convert speech to text by doing mathematical calculations to determine what was exactly spoken.

There are two chief areas of Natural Language Processing (NLP). One is where the computer offers the meaning of the language it has received, which is known as Natural Language Understanding (NLU). The next one, where the process converts the accumulated information from computer language to human language, is known as Natural Language Generation (NLG).

Brief Breakdown of Natural Language Processing Tasks Performed By NLP Software

Natural Language Processing (NLP) in simple terms is Artificial Intelligence technology that identifies and understands natural human languages. Spoken or written human speech is converted into a form that the computers understand all through NLP techniques.

Most of us employ NLP in business mobile apps every day without having any knowledge about it. Online search, spell-checkers, voice assistants, translators; almost all of these embrace Natural Language Processing (NLP) technology.

Below let’s briefly breakdown many NLP tasks that are performed by modern NLP software. So, let’s get started.

NLP Tasks

Text Pair MatchingNatural language inference, Semantic textual similarity, Relation prediction
Sentence ParsingSemantic parsing, Consistency parsing, Dependency parsing
Word TaggingWord Segmentation, Part-of-speech tagging, Word sense disambiguation, Shallow syntax-chunking, Semantic role labeling, Named entity recognition
Text GenerationSimplification, Dialogue, Language modeling, Question answering, Summarization, Machine translation
Text ClassificationSentiment analysis, Text classification, Coreference resolution, Temporal processing

1. Summarization

The task that embraces the shortening of text by recognizing the key parts and crafting a summary is known as summarization. There exist two approaches to text summarization:

  • Extractive Summarization: Recognition of the chief phrases or sentences from the original text and also extracting them from the text.
  • Abstractive Summarization: The new sentences are generated from the original text, where the sentences that are generated may not be presented in the original text.

2. Language Modeling

The Natural Language Processing task that embraces the prediction of the next character/word in a document/text is Language Modeling. It may be used for the following:

  • Image Captioning
  • Optical Character Recognition Machine Translation
  • Handwriting Recognition
  • Text Summarization
  • Spelling Correction

3. Named Entity Recognition

The task that states the recognition of the entities in a sentence is Named Entity Recognition, such as organization, person, location, date, time, etc. along with their classification into the categories.

4. Text Classification

The task that includes assigning the categories/tags to text as per the content is text classification. The text classified can be employed to organize, structure, and categorize any text.

5. Sentiment Analysis

The task that states a wide range of subjective analysis to recognize negative or positive feelings in a sentence, customer review’s sentiment, judging the mood through voice analysis or written text, and other akin tasks is known as sentiment analysis.

6. Part-of-Speech Tagging

The task that includes marking up the words in a sentence, like verbs, nouns, adjectives, adverbs, and other descriptors, is known as Part-of-speech tagging.

How to Use Natural Language Processing in a Mobile App?

A technology that is much more advanced than virtual assistants is known as Natural Language Processing (NLP). Mobile app developers integrate NLP to make the mobile app devices more accessible to individuals with physical abilities. Businesses arrive with advanced solutions for mobile app development employing NLL and Machine Learning. Such solutions allow the users to ask any queries using normal language and get a revert in the same way.

Let’s now check many sorts of mobile apps that utilize NLP technology in distinct sectors.

  • Use of NLP to Drive an Information Engine
  • Use of NLP for Mobile Apps in the Medical Field
  • Use of NLP for Fighting Spams

Wrapping Up

Natural language processing is a technology that may have a perfect impact on the mobile app development world. NLP supports many virtual assistants and apps that can craft mobile devices smarter.

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