The 2022 Definitive Guide to Natural Language Processing NLP

NLP can help insurance companies with data-driven decisions providing insights into customer preferences and usage patterns. NLP can also be used for automated interactions with customers, more accurate risk assessment for underwriting policies efficiently, accurately processing claims faster, and more securely identifying fraudulent claims. While each technology has its own unique set of applications and use cases, the lines between them are becoming increasingly blurred as they continue to evolve and converge. With the advancements in machine learning, deep learning, and neural networks, we can expect to see even more powerful and accurate NLP, NLU, and NLG applications in the future. Virtual assistants configured with NLU can learn new skills from interaction with users. This application is especially useful for customer service because, as the chatbot has conversations with shoppers, its level of responsiveness improves.

NLP focuses on processing and analyzing text data, such as language translation or speech recognition. NLU goes a step further by understanding the context and meaning behind the text data, allowing for more advanced applications such as chatbots or virtual assistants. Natural Language Generation (NLG) is another subset of natural language processing. NLG enables AI systems to produce human language text responses based on some data input. Using NLG, contact centers can quickly generate a summary from the customer call. It is a technology that can lead to more efficient call qualification because software employing NLU can be trained to understand jargon from specific industries such as retail, banking, utilities, and more.

All About Natural Language Understanding

The benefits of NLP systems are that they break down text into words and phrases, analyze their context, and perform tasks like sentiment analysis, language translation, and chatbot interactions. Moreover, OpenAI’s advanced language models empower comprehensive text analysis, while LangChain’s specialized NLP solutions enhance data management. By combining contextual understanding, intent recognition, entity recognition, and sentiment analysis, NLU enables machines to comprehend and interpret human language in a meaningful way. This understanding opens up possibilities for various applications, such as virtual assistants, chatbots, and intelligent customer service systems. Together, these technologies enable computers to process human language in text or voice data and

extract meaning incorporated with intent and sentiment.

In recent years, businesses, brands and increasingly dependent on technology to help them complete their daily tasks more efficiently. Artificial intelligence is playing a major role in this trend because it’s essentially the backbone of many assistive technologies. In the home, office, classroom and beyond, people may not even realize how often they’re interacting with AI-powered solutions. Akkio is an easy-to-use machine learning platform that provides a suite of tools to develop and deploy NLU systems, with a focus on accuracy and performance.

Intent Classification

Behind the scenes, sophisticated algorithms like hidden Markov chains, recurrent neural networks, n-grams, decision trees, naive bayes, etc. work in harmony to make it all possible. Imagine planning a vacation to Paris and asking your voice assistant, “What’s the weather like in Paris? ” With NLP, the assistant can effortlessly distinguish between Paris, France, and Paris Hilton, providing you with an accurate weather forecast for the city of love.

A task called word sense disambiguation, which sits under the NLU umbrella, makes sure that the machine is able to understand the two different senses that the word “bank” is used. All these sentences have the same underlying question, which is to enquire about today’s weather forecast. Questionnaires about people’s habits and health problems are insightful while making diagnoses. In this section, we will introduce the top 10 use cases, of which five are related to pure NLP capabilities and the remaining five need for NLU to assist computers in efficiently automating these use cases. Figure 4 depicts our sample of 5 use cases in which businesses should favor NLP over NLU or vice versa.

NLG (Natural Language Generation)

Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. The voice assistant uses the framework of Natural Language Processing to understand what is being said, and it uses Natural Language Generation to respond in a human-like manner. There is Natural Language Understanding at work as well, helping the voice assistant to judge the intention of the question. Natural Language Understanding (NLU) is a field of computer science which analyzes what human language means, rather than simply what individual words say. Arria’s Investment Analyst uses NLG to mine investment data for insights, anomalies, and implications. It then generates investment commentary with the click of a mouse, and using narrative models, the commentary is customized to emulate your firm’s voice.

• NLU tools should be able to tag and categorize the text they encounter appropriately.
• As NLU technology continues to advance, voice assistants and virtual assistants are likely to become even more capable and integrated into our daily lives.
• Understanding how customers and prospects think about and interact with your brand is essential to creating targeted, successful marketing strategies and campaigns.
• You can create your free account now and start building your chatbot right off the bat.
• It is crucial to natural language processing applications such as structured search, sentiment analysis,

  question answering, and summarization.

While NLU focuses on interpreting human language, NLG takes structured and unstructured data and generates human-like language in response. NER uses contextual information, language patterns, and machine learning algorithms to improve entity recognition accuracy beyond keyword matching. NER systems are trained on vast datasets of named items in multiple contexts to identify similar entities in new text. As a result, algorithms search for associations and correlations to infer what the sentence’s most likely meaning is rather than understanding the genuine meaning of human languages. The integration of neural networks, especially deep learning, brought about a transformative change in NLP.

Automated Document Processing

Identifying their objective helps the software to understand what the goal of the interaction is. In this example, the NLU technology is able to surmise that the person wants to purchase tickets, and the most likely mode of travel is by airplane. The search engine, using Natural Language Understanding, would likely respond by showing search results that offer flight ticket purchases. The right market intelligence software can give you a massive competitive edge, helping you gather publicly available information quickly on other companies and individuals, all pulled from multiple sources. This can be used to automatically create records or combine with your existing CRM data. With NLU integration, this software can better understand and decipher the information it pulls from the sources.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. It allows computers to understand and respond to human language, enabling them to perform tasks like translation, sentiment analysis, information extraction, and automated customer service. NLP is crucial in processing and analyzing the vast amounts of data generated daily, helping businesses gain insights, improve decision-making, and enhance customer experiences. Natural language processing (NLP) is a field of AI that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a valuable way.

Join us as we explore Natural Language Processing in AI and answer questions like ‘What is Natural Language Processing? ’ and explain why NLP is a technology about understanding human communication’s very fabric. NLU is the set of AI techniques and applications aimed to understand human language, and convert it into data. NLG, instead, represents the set of techniques that use structured data to generate meaningful narratives.

Akkio offers an intuitive interface that allows users to quickly select the data they need. NLP deals with language structure, and NLU deals with the meaning of language. “I love eating ice cream” would be tokenized into [“I”, “love”, “eating”, “ice”, “cream”].

With the surface-level inspection in focus, these tasks enable the machine to discern the basic framework and elements of language for further processing and structural analysis. These notions are connected and often used interchangeably, but they stand for different aspects of language processing and understanding. Distinguishing between NLP and NLU is essential for researchers and developers to create appropriate AI solutions for business automation tasks. The fascinating world of human communication is built on the intricate relationship between syntax and semantics. While syntax focuses on the rules governing language structure, semantics delves into the meaning behind words and sentences.

Read more about What is the difference between NLP and Use Cases here.