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12 Real-Life Examples of NLP

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Do you know how the science behind virtual assistants like “Siri” and “Google Maps” works to enable them to get voice commands and interpret and respond to them? The NLP (natural language processing) technology that forms the basis of this phenomenon has a crucial function in generating artificial intelligence, which can mimic human communication.

Here, we will discuss exemplification (real-life) events of NLP in real life and clarify the ways of NLP application in a business context with the aim of process optimization.

While exploring various applications of NLP, we commence with the following common use cases:

  1.  Search Engine Functionality

Search engines utilize NLP methods in order to recommend the appropriate results based on the user intent and their past searches. And it explains instances like Google which caters to the anticipated query of the users and understands the context cues and the intended meaning behind the search input. This tends to create the illusion that you have already finished forming your thought and it will complete itself before you even get a chance to express it, as it comes out in the form of a math equation being calculated as you enter it, or the flight number giving you a real-time update when you enter it.

  2. Language Translation

NLP, language translation, occupies a central position among the essential applications. Traced back to the pioneering efforts of Georgetown and IBM in the 1950s, starting from NLP-based translation machines is a momentous milestone for automatic language translation. Today’s translation apps leverage the potential of NLP and deep learning methods to translate both textual and oral content across an array of languages, thereby offering services to people who use different languages all around the globe.

3.   Survey Analytics

In the world of data analytics, interpreting survey results is a huge task in itself. When it comes to open-ended as well as unstructured textual inputs, the task becomes even more difficult. NLP, in conjunction with other artificial intelligence (AI) disciplines, endeavors to address this challenge by facilitating advanced analysis of survey data. Leveraging NLP competencies, organizations can derive actionable insights from survey responses efficiently and accurately, without necessitating extensive human intervention. Text processing libraries like “SpaCy” and “Gensim” serve as two examples of tools that streamline the extraction of valuable data from unstructured textual information.

4.  Sentiment Analysis

The analysis of attitude, or opinion mining, is a challenging NLP tool for understanding the mood in the textual data. The textual type of feedback can be analyzed using the techniques of sentiment analysis to determine whether the text expresses a positive, negative or neutral sentiment. This skill is very important to understand how the customer feels about the brands and the products and also helps the organizations to track and deal with customer feedback.

5.    Autocomplete & Autocorrect

NLP application has acquired its own identity and is widely used to assist users in different situations. Autocomplete does what it says, predicting the next words or sentences based on the context of the text entered, making use of NLP algorithms and machine learning models to aid in text completion. Comparably, when it comes to the autocorrect feature, NLP is also used to find and correct spelling errors in textual inputs and this way the experience of the user and the text communication become more accurate.

6.    Spellcheck

It is also imperative to mention the well-known application of NLP which is spelling check functionality that is ubiquitous in modern digital interfaces. This function identifies and informs the user when there are spelling errors in the textual inputs. As a consequence, users’ written communication will be clear and accurate. Corporately, spellcheck is of great help by bringing about data integrity through the identification and rectification of typing errors and false information in textual databases.

7.    Email filters

Spam filters are certainly the most famous NLP solutions, which are widely used on servers of the internet. In the beginning, these filters were designed only to identify spam mails. Later, they were modified to comprise a wide range of use cases. For instance, a few days ago, Gmail came up with a new improved classification device in which emails are categorized to be either in the primary, social, or promotional boxes according to their content. This NLP advanced technology is applied to the management of the users’ inboxes in such a way as to optimize it and ensure that only the important messages are considered first.

8.    Chatbots

Chatbots are an NLP practical application that has a huge impact especially when it comes to customer service. These automated systems facilitate rapid, on-demand responses to customer inquiries, thereby enhancing user experience, especially for smaller enterprises lacking dedicated customer support personnel. 65 percent of the decision-makers in customer service recognized the contextual understanding capabilities of chatbots, with 52 percent of them having confidence in the ability of the chatbots to automate decision-making processes by making decisions based on customer inputs. Chatbots are capable of doing quite a lot from answering FAQs, booking appointments, handling orders and even up-selling their products.

Modern AI-powered personal assistants such as “Siri” (Apple), “Alexa” (Amazon) and the “Assistant” (Google) utilize voice recognition technology together with NLP (Natural Language Processing) algorithms to understand and respond to user questions. This development in capabilities has resulted in assistants who are no longer limited to one-way interactions as they now engage in tasks ranging from online shopping assistance involving order finalization and payment processing. By incorporating continuous learning and refinement, the smart assistants improve their ability to support more and more specialized and personalized assistance.

9.   Social Media Monitoring

Natural Language Processing (NLP) is undoubtedly a game changer in the space of social media monitoring through its ability to derive sense from massive numbers of user comments and feedback. Sentiment analysis algorithms are the tools that social media monitoring systems use to analyze comments and determine the overall feelings of the audience, helping companies to keep their reputations intact and giving them the possibility to receive useful inputs from their clients.

10.   Customer Service Automation

    Customer service uses NLP for its maximum output with work of ease. Automation which simplifies operations with responses to repetitive inquiries and ticket tracking based on content analysis. This consequently saves customer service teams the time from handling things that only require simple human intervention, and in its place focus on the issues that require human attention, which in return will boost productivity and efficiency.

    11.    Analyzing Converted Text/Optical Character Recognition (OCR)

      NLP finds an important role in text analysis, specifically in the case of OCR, that is when documents or images are converted into machine-readable text. OCR technology provides the ability to automate data extraction processes from which further NLP applications such as sentiment analysis and language translation can be engaged. A Google Translate is one of the examples, it uses OCR to extract texts from web pages, pictures or documents and transforms them into readable text.

      Speech recognition (ASR or STT) is a software technology that can convert the human speech it receives into sound waves and acoustic sounds, and it can transform them into a machine-readable digital format. ASR is implemented in a manner that breaks the speech audio into small parts, called tokens, which are studied for the most likely word in the specific language context. Via the help of algorithms, the sound will be transmitted into textual forms.

      Speech recognition technology is one of the main use cases that can be seen in the phone integration of voice search which is the function within smartphones. Thus, this function allows users to tell the search engine what they want by speaking directly into it; after that, the words are transcribed into text so that the search can start.

      12.     Natural Language Generation

        In addition to Natural Language Processing (NLP), Natural Language Generation (NLG) is a part of the spectrum of customer-facing applications that include smart assistants, chatbots and other customer service bots. NLG works based on data that it employs to pull off written or spoken narratives.

        This method involves several series of following stages:

        Content analysis: Data will be treated to filter information to fine it and distinguish the main topics and establish relationships between them.

        Data understanding: There is a process of statistics that is interpreted to distinguish patterns and contextual clues, commonly through the use of ML techniques.

        Document structuring: The strategy of the narrative is constructed which is determined by the nature of data inputs. The plan is then structured outlining the framework of the narrative.

        Sentence aggregation: Most essential elements of the sentence are merged to provide the reader with the main idea of the cause in discussion.

        Grammatical structuring: Rules of grammar are used to form meaningfulness and linguistic correctness to the natural flow of the generated sentence.

        Language presentation: At the end, the result and the format in which it is presented are determined.

        Through these systematic processes, NLG facilitates the creation of coherent and contextually relevant narratives, enriching user experiences across a spectrum of interactive platforms.

        Final Discussion/Last but not Least

        NLP’s impact is undeniable, knitting itself into the fabric of our daily interactions with technology. From search engines to virtual assistants and from social media monitoring to chatbots, NLP is transforming how we work, communicate and access information. As NLP continues to evolve, the possibilities for seamless human-computer interaction and a more intuitive technological environment are imagined as ceaseless.

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