What is Natural Language Processing ? Applications & Future Possibilities

Current Applications and Future Possibilities of Natural Language Processing (NLP)

What is Natural Language Processing ? Applications & Future Possibilities

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) technology. The role of natural language processing in AI enables machines to efficiently understand the language of humans. We all know that it is not that easy to teach a machine to easily understand how humans interact with each other. Here, NLP technology comes into the picture.

Many scientists have still explored the efficiency of artificial intelligence and its related technologies. On the NLP front, it grabbed the market attention with its intellectual capabilities. NLP technology has been posing significant breakthroughs in the market.

Through this blog, we thought to give a brief introduction to NLP technology. So, let’s begin!

What Is NLP Technology and How It works?

Natural language processing in AI is a hot concept across the mobile app development industry. NLP technology in combination with AI is used for developing next-generation conversational apps that act as an interface between machines and humans. Driven by language processing abilities, NLP apps can listen, read, understand, analyze, and respond to human languages in a valuable way.

NLP methods are heavily dependent on machine learning techniques to interpret human spoken languages. Here is the actual process of natural language processing applications. A normal conversation between machines and humans using NLP technology will be like as below:

Step-1: A human start a conversation with the machine
Step-2: The NLP-powered machine then stores the audio clip
Step-3: The in-built algorithms works on converting Audio to human-readable text
Step-4: The text data will be processed
Step-5: Again, data will be converted into Audio
Step-6: Finally, NLP-powered applications play audio and reply to the users in a fraction of seconds

This was the actual working process of NLP-driven machines.

What Are The Applications Of NLP?

Here are a few types of Natural Language Processing (NLP) applications.

Significant Types Of Natural Language Processing Apps

  • Language translation applications

NLP technology blog1
Do you wish to learn a new language?

No worries. NLP-powered language assistants are there to support you. Natural language processing techniques make online learning more personalized and interactive.

Learning a new language is not an easy task for learners. But the online language learning and translation apps make the learning process hassle-free and offer users a localized interaction with the mobile app. Users can communicate with the tutors or virtual sessions more effectively using these apps.

NLP-powered language translation apps help users in many ways. Google Translate, iTranslate, TripLingo are a few of the best examples of NLP systems that allow people to interact and communicate with the app using their native language and get instant responses for their queries.

NLP Mobile Apps Development To Check The Grammatical Errors Of Texts

Every content writer must aware of Grammarly and Microsoft Word. It is essential to promote quality content for an organization for implementing the best digital brand promotion strategies.

Grammarly-like applications help writers to perform spell checks and find grammatical errors in the text they developed with ease. It is all because of in-built NLP algorithms. The role of natural language processing in AI apps can accurately check the correctness of your written text.

This will reduce the complexity in writing documents and also saves the time of users in correcting grammatical errors manually. Thanks to such wonderful intelligent technology.

• Interactive Voice Response (IVR) applications

IVR is an automated telephony system used for integrating organizations with callers, collecting data, and routing a call to the right persons. It is one of the significant examples of NLP systems.  NLP-based IVR apps help call centers to provide faster connectivity and response services to their customers.

The NLP technology is used in IVR systems to understand the spoken language of a customer. Then with the help of language generation capabilities, NLP helps an IVR system to produce accurate responses to the callers.

NLP-powered Personal Assistant Applications

Since the use of smart home devices or IoT sensor devices are increasing, the importance of Natural Language Processing innovations is propelling worldwide.

Siri, Cortana, OK Google, and Alexa are the best examples of NLP applications. These digital voice assistants with their in-built NLP capabilities can communicate just like a human. These assistants can interpret human languages and perform tasks that they are assigned to do. AI and NLP digital voice assistants also do music playback, set alarms, play audiobooks, and provide real-time information on weather and traffic conditions.



    Process of Natural Language Processing (NLP)

    Semantic and syntactic analysis are the two major procedures that are utilized to accomplish NLP-related tasks.

    1. Syntactic Analysis

    Syntax refers to forming a sentence with a group of words in a meaningful way. In NLP, this procedure is used to measure how natural language supports the rules of grammar.

    Some computer algorithms are utilized to apply grammar rules to a collection of words and find out the actual meaning from them.

    Here are some of the syntax methods that can be used:

    1. Lemmatization: It reduces the complexity of a word for making it easily understandable
    2. Morphological segmentation: It divides words into distinct parts
    3. Word segmentation: It devises a big text into separate units to improve its readability.
    4. Parts-of-speech: it identifies the part of speech for every single word
    5. Parsing: It checks the grammar of a sentence
    6. Sentence Break: It limits the length of each sentence to a big text.
    7. Stemming: It removes the modified words from their original form.

    2. Semantic Analysis

    Semantics means the actual meaning of the given text. Semantic analysis is the difficult procedure of Natural Language Processing (NLP), which is not fully resolved to date.
    In this process, some computer algorithms are applied to analyze and understand the meaning of words, and to know the arrangement of sentences.
    The key methods of semantic analysis include:

    1. Named Entity Recognition (NER): It categorizes the parts of a text into fixed groups. It means, NER identifies the same group of elements like people’s names, and place names, etc.
    2. Word Sense Disambiguation: It provides exact meaning to a word based on the situation.
    3. Natural Language Generation: It converts semantic meanings and translates them into human-understandable language.

    Now, we’ll have a brief discussion on the growth of NLP technology by 2024.

    Increased smartphone usage and adoption of cloud systems are two major factors behind the growth of the NLP and AI app development. Companies are using AI+NLP mobile apps and integrated systems to ensure better customer experiences.

    According to the market reports, the market size of NLP technology is expected to increase from USD 10.2 billion in 2019 to USD 26.4 billion by 2024.

    NLP-based sentiment analysis apps are more popular in the market. These NLP mobile apps can analyze sentiments of diverse datasets. This process can identify positive, negative, and neutral sentiments from the given text. Sentiment analysis applications are expected to grow at the highest rate during the forecasted period of 2019-2024.

    Generally, the sentiment analysis is used for getting valuable insights from social media reviews, the company’s product reviews for making data-driven decisions. Hence, using NLP technology-based applications, organizations understand their customer requirements which result in improved brand recognition in the market.

    3. Machine Translation

    Machine translation (MT) refers to completely automatic software that can translate content from one language to some other languages. The perfect example of MT is ‘Google Translate’.

    Machine translation is also known as instant translation or automated translation. It will even translate the essential data and speech into any other language without any human involvement.
    The machine translations are divided into four types

    • Rule-based Machine Translation (RMT)
    • Statistical Machine Translation (SMT)
    • Neural Machine Translation (NMT)
    • Hybrid Machine Translation (HMT)

    4. Speech Recognition

    The major role of natural language processing in AI falls in recognizing human speech. Speech recognition technology has been around the world for the past 50 years.

    Today, speech recognition is a trending topic that is a part of various products, including voice assistants (Siri, Google Assistant, Cortana, etc.). This speech recognition app replaces other time-taking methods like typing, selecting, or clicking the text in any other format.

    5. Chatbots

    AI conversational Chatbots applications are the best examples of NLP systems and reflects the importance of Natural Language Processing technology for businesses to enhance brand value in this digital age.

     Artificial Intelligence + NLP powered Chatbot apps ensure high-level and instant virtual assistance for the customer’s queries online. Chatbot applications will minimize human efforts, cost, and provide efficient solutions that save time. Soon, intelligent AI-powered Chatbots will be providing personalized assistance to users.

    6. Automatic Summarization

    Automatic summarization is a method of creating a short, precise, and articulate summary of a longer text document. It comprises of diminishing a text and creating a compact new form that contains its most applicable information. It will be especially helpful to condense large bits of unstructured data, for example, academic papers.

    There are two distinct methods for utilizing NLP for Summarization: the foremost extricates the significant data within a text and uses it to outline (extraction-based summarization);. At the same time, the second applies profound learning strategies to paraphrase the content and produce sentences that are not available in the primary source.

    7. Spell Checking

    Spell Checker is the most commonly used software tool by employees in every organization, especially the Text Editors or Content Developers. This awe tool recognizes any spelling errors in a sentence and rectifies them with correct spelling.

    One of the most realistic examples of the spell-checking tools is the ‘Grammarly’ Application. Grammarly is an online (Free/Paid) grammar checker that examines your content for a wide range of errors, from grammatical errors to sentence structure mistakes and past.

    8. Question replying (QA)

    Question-Answering is turning out to be increasingly more popular thanks to the apps like Siri, chat boxes, virtual assistants, and OK Google. A QA application is a framework prepared to answers the questions raised by humans. It might be utilized as a text-only interface or as a verbally expressed system. While they offer an incredible guarantee, they have a long way to go still. It remains a significant test, particularly for web search engines, and is one of the principles uses of NLP.

    Future Possibilities of NLP

    Natural language processing in AI as technology continues to grow. NLP and AI apps will be more user-oriented and promise more personalized experiences.

    An AI virtual assistant like Siri is the best natural language processing in AI example. It can solve many complicated queries assessing the implications alongside the literal, which means of the query asked. The NLP applications are not just restricted to resolving customer questions or offering customized shopping but have, however, has advanced into a greater technological help of sorts. In the present day, Natural Language Processing can be trained to give a list of mistakes, if someone uses NLP to ask, “What’s wrong with my network?”.

    In the coming years, NLP will be in a position to find out the user’s actual intention like she/he wants his network constant for access.

    The future of natural language processing in AI will be thrilling. The technology advancements will be more excited with the innovations derived from the combinations of AI, NLP, IoT, and ML-like digital technologies. Since the importance of NLP will be high in building gesture and facial recognition apps, the scope for this technology would be promising in the near term future.

    USM, a leading Artificial Intelligence app development service provider, delivers intelligent AI and NLP solutions that understand human languages and respond to their queries with more accurate answers.

    Contact us now!

      • Keep working, wonderful job! Exactly the info I had to know about about Natural language processing Services in India

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