Natural Language Processing in Everyday Use Cases

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Natural language processing (NLP), one of the most challenging and alluring fields of artificial intelligence, refers to the communication between machines and humans. How does it work? What is the potential of natural language processing services for the business audience? Making computers comprehend the intricacies of speech is no small feat, but the demand for NLP services boosts the continuous technology growth. NLP service providers do their utmost to harness the algorithms’ potential and create advanced solutions that’ll satisfy the audience.Copy HTMLCopy text

Hopefully, they actually don’t. It’s not like our well-worn PCs hear us scolding them for being to slow with the installation of that oh-so-important system update right when we’re in a hurry to print some documents. Neither do they eavesdrop on us casually mentioning to our partners the idea to buy a new computer next month and ditch the old one? They really don’t, do they? To anyone who’s seen sci-fi movies like Terminator, Matrix series, or any other iconic film on the Rise of the Machines, such a perspective seems a bit disturbing to say the least. 

Jokes aside, do computers really comprehend human language?

Yes, natural language processing allows computers to communicate with users and understand what we want them to deduce. To some extent, at least for the moment.

Challenges of natural language processing

Why is speech, one of the greatest phenomena in our lives, so hard to explain to machines? We humans often don’t understand each other, not just in text communication but even face to face. Even if using the same language we need to comprehend accents, speech impediments, slang, sarcasm, and various figures of speech. And to think that machines are learning to fish out the meaning from context and grasp the intended message!

Human language is full of exceptions and ambiguities, making it hard for machines to learn. What helps computers with understanding speech is reducing vague and unclear expressions. This can be achieved by, for example, replacing pronouns (she, her, it, his, etc.) with the actual words, we’re referring to.

Coreference resolution (CR) could be the answer to NLP algorithms’ problems with multiple pronouns in sentences. CR allows for finding mentions of the questioned matter within the text referring to it. The distinguished expressions are grouped and, as mentioned above, replaced with nouns. Using coreference resolution it’s possible to support information extraction, machine translation, text understanding, document summarization, and sentiment analysis. As a result, creating unambiguous sentences is a lot easier to understand by non-human investigators.

As a result, sentences don’t require additional context for the machine to understand their meaning, the appliance becomes self-contained. 

Real-life applications of natural language processing

Natural language processing enables computers to read text, hear speech, assess sentiments, interpret the messages, and even determine their importance. 

The demand for NLP applications grows, as the ability to understand human language proves to be far easier to be naturally mastered by humans than by machines. Employing natural language processing services in the offered digital solutions can change the way clients interact with computers. And that’s for the better!

Companies generate loads of data in the course of their operations. Each and every day more unstructured, text-formatted pieces of information land on the pile and await processing to benefit the business. Given the circumstances, an NLP real-life application would be a life saver.

Another great room for NLP to maneuver is the dynamically growing field of chatbots. Natural language processing helps machines to see past text-based user inputs and differentiated “hellos” from “goodbyes”. Algorithms help chatbots communicate with clients, understand the intent of the conversation and respond to incoming queries. What’s the final result? No more unnatural conversations or misunderstandings caused by AI getting lost in translations. With NLP, chatbots deliver outstanding user experience, improving overall satisfaction.

Natural language processing techniques can be employed also in survey analysis. When companies dispatch satisfaction surveys to customers following each transaction, the data volume grows exponentially. Retrieving vital information from documents helps enterprises to collect and analyze customers’ feedback on offered products or services. In the case of processing large resources, providing insights by humans is nearly impossible. Natural language processing algorithms analyze the surveys, generate conclusions, and provide accurate information, helping enterprises design better solutions. In such cases, the human touch is necessary for the retrieval and analysis stage where machines are helpless. Replacing human input while browsing through extensive databases contributes also to better cost management, as machines work error-free 24/7. 

Human resources departments can also benefit from incorporating NLP in process execution. The more attractive the vacancy, the greater the influx of candidates’ resumes, which leads to more challenges in deciding on the best future hire. In such cases, any help with weeding out non-matching applicants is a big relief for recruiters. NLP does the job of HR professionals going through piles of resumes and filtering candidates manually. Using techniques like informed extraction with named entity recognition, algorithms can extract desired information ( skills, language proficiency, location, or education). Building on these techniques, machines can group the candidates in their entirety into two categories: a great match or a mismatch for the position in question. From this point on, HR staff takes over the process and schedules meetings only with the candidates that are at least a preliminary fit for the role. Benefits? Reduced experts’ workload, filtering based on strong arguments, and bypassing the risk of biased decision making, as machines assess clear and non-discriminating factors. 

Other examples of successful NLP implementation include:

  • search autocorrect and autocomplete
  • social media monitoring
  • language translators
  • targeted advertising
  • document digitization
  • smart assistants
  • digital phonecalls 

The future of natural language processing services

What’s the direction NLP is heading towards? Teaching computers to understand human language as organically as we do has a bright future. The market for NLP services is expected to grow and increase its worth, as companies lean towards transforming their operations digitally. Employing algorithms to better understand clients, documents, and data requires modern solutions. And that’s what NLP is – a future-proof technology that changes the way business processes are executed. 

Natural language processing services will continue to grow. They will facilitate company development with process automation, and transform operations digitally.