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Natural Language Processing (NLP) Basics

Unveiling the Power of Natural Language Processing: Top 10 Real-World Applications

Dec 05, 20236 min read

NLP makes our lives much easier. Learn about the top 10 most popular NLP applications and how they have an impact on our lives.

By Alex Gao

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In a world where technology constantly evolves, Natural Language Processing (NLP) is a formidable force shaping our daily interactions with machines. From voice-activated personal assistants to advanced chatbots and beyond, NLP has become the backbone of several groundbreaking applications that enhance user experiences and streamline business processes.

Understanding NLP

At its core, Natural Language Processing refers to a machine's ability to comprehend and interpret human language. This subfield of artificial intelligence (AI) has proven to be a game-changer, enabling seamless communication between humans and devices. Bill Gates aptly says, "NLP is the pearl in the crown of AI."

The applications of NLP are vast and varied, touching almost every aspect of our lives. This post will delve into NLP's top 10 real-world applications, showcasing its transformative impact on industries and user experiences.

Content creation

Content creation has become a popular use case for large language models (LLMs), among the most viral NLP technologies in 2023. LLMs enable users to generate written content, from blogs and articles to short stories, summaries, scripts, questionnaires, surveys, and social media posts. The quality of these outputs depends on the details provided in the initial prompt.

Even if LLMs cannot generate high-quality content, you can still use them for generating creative ideas. AI-powered content creativity has become a common marketing practice, where LLMs generate ideas or inspiration for marketing content. In addition, tools like DALL-E, MidJourney, Stable Diffusion, and others allow users to create images based on a written prompt for visual content creation.

Machine translation

NLP facilitates language translation, powering services like Google Translate and Skype Translator. Multinational companies employ NLP to convert product information and marketing content into various languages, enhancing communication with diverse customer bases.

Since late 2022, advanced NLP models, such as OpenAI's GPT series, have started to compete with established commercial translation tools, providing even higher quality translations for various language pairs. However, there are challenges when translating "low-resource" or "distant" languages, which are less commonly used or have fewer resources available for translation.

Code development

Generative AI (GenAI) has revolutionized code development by introducing tools that assist programmers in various phases of the software development lifecycle. AI generates code snippets through techniques like GANs (Generative Adversarial Networks), aiding developers in prototyping, debugging, and even offering solutions to complex problems. This technology enhances efficiency by automating repetitive tasks, accelerating development cycles, and offering novel perspectives on coding challenges.

Search

GenAI, powered by NLP, transforms how we approach search, enabling more intuitive and precise search capabilities. Traditional keyword-based searches often produce limited or irrelevant results. However, GenAI can interpret the meaning behind search queries, considering nuances, synonyms, and even context, providing more accurate and relevant results. GenAI can evolve to adapt to user behavior, feedback, and emerging language trends, continually enhancing the search experience.

However, content generated by AI engines like Bard or ChatGPT may only sometimes be accurate. LLMs are prone to hallucinations and tend to invent facts and figures. Therefore, it’s crucial to double-check any factual information presented by LLMs.

Transcription

NLP algorithms, equipped with machine learning and speech recognition models, can accurately interpret and transcribe audio or video content into text despite variations in accents, intonations, and background noises. This technology allows for seamless communication and documentation, saving humans significant time.

Unlike traditional transcription software, NLP enables these tools to infer the context and meaning of statements supplied via audio. This technology not only helps transcribe webinars, interviews, meetings, or dictations but also plays a critical role in accessibility by providing real-time captioning for people who are hard of hearing. Furthermore, it facilitates the indexing and searchability of audio and video content.

Chatbots in e-commerce

Revolutionizing customer service in the e-commerce sector, chatbots have become indispensable for brands like Amazon. These AI-driven assistants handle post-sales interactions efficiently and provide pre-sales support, offering a personalized and instant customer experience.

Take Amazon as an example; it improved customer service by implementing NLP-driven chatbots to handle product order issues and replacing the frustrating phone wait times. Customers can quickly request refunds by interacting with the Amazon chatbot, which mimics human responses. These chatbots also assist with pre-sales inquiries.

Sales automation

GenAI tools such as ChatGPT offer a transformative edge in automating various segments of the sales process, streamlining workflows from lead generation to forecasting. These tools utilize machine learning and data analytics to enhance lead generation by identifying patterns and behaviors and facilitating targeted outreach to potential customers. Through personalized communication, AI-generated content tailors interactions, nurturing leads by providing relevant information, thus fostering stronger connections.

Additionally, AI aids in lead qualification by analyzing data points, predicting customer intent, and guiding sales teams toward the most promising opportunities. AI algorithms significantly refine lead scoring, evaluating lead quality based on numerous factors and behaviors. Finally, employing predictive analytics, AI assists in forecasting sales trends, enabling businesses to make informed decisions and strategize for future growth, revolutionizing the sales process efficiently and precisely.

Conversational user interface

Another trendy and promising application is interactive systems. Many well-recognized companies are betting big on CUI (conversational user interface). CUI is the general term for computer user interfaces that simulate conversations with real human beings. The most common CUIs in everyday life are Apple's Siri, Microsoft's Cortana, Google's Google Assistant, Amazon's Alexa, etc.

In addition, developers can embed CUIs into cars, especially electric vehicles. NIO, a car manufacturer dedicated to designing and developing EVs, launched its own CUI set named NOMI in 2018. Visually, the CUIs in cars can work in the same way as Siri. Drivers can focus on steering the vehicle while asking the CUI to adjust the A/C temperature, play a song, lock windows/doors, navigate drivers to the nearest gas station, etc.

AI-powered call center

Call centers are vital for customer experience, but manual call reviews are impractical due to high volumes. NLP introduces an AI-powered call quality control engine that automates issue detection, ensuring adherence to guidelines. This engine elevates check rates from 2%-3% to 100%, significantly reducing costs and human resources.

Adopting NLP also enhances call operation services. Traditionally, operators navigate extensive manuals to handle calls, a time-consuming process often lacking satisfactory solutions. AI-powered call centers streamline customer interactions. Upon a customer's call, the system swiftly retrieves customer and order information, providing operators with key details. Conversations are recorded with a live chat log on the screen, enabling efficient handling of tricky questions or complaints. With a robust deep learning model, your service can consistently provide >99% accurate answers and handle complaints adeptly.

Intelligent outbound calls

Believe it or not, some phone calls you receive are not from humans! Chances are that it is a robot on the other side of the call. Many companies leverage NLP technologies to build AI-driven interactive voice responses (IVR) systems like Google Duplex to handle outbound calls, reducing operational costs for marketing purposes.

Intents are usually very helpful when dealing with calls like these. An intent is a group of sentences or dialects representing a particular user's intention. For example, "weather forecast" can be an intent that can trigger different phrases. See the picture of a Google Dialogflow example below. Intents can be organized together to accomplish complicated interactive human-computer conversations like making a reservation at a restaurant, booking a flight, etc.

Summary

In conclusion, the ever-expanding landscape of Natural Language Processing has reshaped how we interact with technology, leaving an indelible mark on various aspects of our daily lives. From creative content generation to the precision of language translation and the efficiency of sales automation, NLP is a testament to the synergy between artificial intelligence and human communication.

As we navigate this era of technological marvels, the potential applications of NLP continue to unfold, promising an exciting future where seamless human-machine communication becomes the norm rather than the exception.