6 Real-World Examples of Natural Language Processing

Employee-recruitment software developer Hirevue uses NLP-fueled chatbot technology in a more advanced way than, say, a standard-issue customer assistance bot. In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates. Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. You can also find more sophisticated models, like information extraction models, for achieving better results. The models are programmed in languages such as Python or with the help of tools like Google Cloud Natural Language and Microsoft Cognitive Services.

NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. By applying NLP techniques, companies can identify trends and customer feedback in order to better understand their customers, improve their products and services, create more engaging content, and analyze large amounts of unstructured data. Deeper Insights empowers companies to ramp up productivity levels with a set of AI and natural language processing tools. The company has cultivated a powerful search engine that wields NLP techniques to conduct semantic searches, determining the meanings behind words to find documents most relevant to a query. Instead of wasting time navigating large amounts of digital text, teams can quickly locate their desired resources to produce summaries, gather insights and perform other tasks.

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You must also take note of the effectiveness of different techniques used for improving natural language processing. The advancements in natural language processing from rule-based models to the effective use of deep learning, machine learning, and statistical models could shape the future of NLP. Learn more about NLP fundamentals and find out how it can be a major tool for businesses and individual users. Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few. In contrast to the NLP-based chatbots we might find on a customer support page, these models are generative AI applications that take a request and call back to the vast training data in the LLM they were trained on to provide a response. It’s important to understand that the content produced is not based on a human-like understanding of what was written, but a prediction of the words that might come next.

Natural language understanding (NLU) allows machines to understand language, and natural language generation (NLG) gives machines the ability to “speak.”Ideally, this provides the desired response. Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction.

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If a negative sentiment is detected, companies can quickly address customer needs before the situation escalates. By extracting meaning from written text, NLP allows examples of natural languages businesses to gain insights about their customers and respond accordingly. More than a mere tool of convenience, it’s driving serious technological breakthroughs.

Rajeswaran V, senior director at Capgemini, notes that Open AI’s GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says. These models can be written in languages like Python, or made with AutoML tools like Akkio, Microsoft Cognitive Services, and Google Cloud Natural Language. “The decisions made by these systems can influence user beliefs and preferences, which in turn affect the feedback the learning system receives — thus creating a feedback loop,” researchers for Deep Mind wrote in a 2019 study.