NLP vs NLU vs. NLG: the differences between three natural language processing concepts

NLP vs NLU vs NLG: Understanding the Differences by Tathagata Medium

nlu vs nlp

This understanding opens up possibilities for various applications, such as virtual assistants, chatbots, and intelligent customer service systems. Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services. A subfield of artificial intelligence and linguistics, NLP provides the advanced language analysis and processing that allows computers to make this unstructured human language data readable by machines.

Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions. Another key difference between these three areas is their level of complexity. NLP is a broad field that encompasses a wide range of technologies and techniques, while NLU is a subset of NLP that focuses on a specific task. NLG, on the other hand, is a more specialized field that is focused on generating natural language output. Symbolic AI uses human-readable symbols that represent real-world entities or concepts.

Entity recognition, intent recognition, sentiment analysis, contextual understanding, etc. NLU enables machines to understand and interpret human language, while NLG allows machines to communicate back in a way that is more natural and user-friendly. By harnessing advanced algorithms, NLG systems transform data into coherent and contextually relevant text or speech. These algorithms consider factors such as grammar, syntax, and style to produce language that resembles human-generated content. Language generation uses neural networks, deep learning architectures, and language models. Large datasets train these models to generate coherent, fluent, and contextually appropriate language.

The advent of recurrent neural networks (RNNs) helped address several of these limitations but it would take the emergence of transformer models in 2017 to bring NLP into the age of LLMs. The transformer model introduced a new architecture based on attention mechanisms. Unlike sequential models like RNNs, transformers are capable of processing all words in an input sentence in parallel. More importantly, the concept of attention allows them to model long-term dependencies even over long sequences. Transformer-based LLMs trained on huge volumes of data can autonomously predict the next contextually relevant token in a sentence with an exceptionally high degree of accuracy. NLP systems learn language syntax through part-of-speech tagging and parsing.

Machine learning uses computational methods to train models on data and adjust (and ideally, improve) its methods as more data is processed. The “suggested text” feature used in some email programs is an example of NLG, but the most well-known example today is ChatGPT, the generative AI model based on OpenAI’s GPT models, a type of large language model (LLM). Such applications can produce intelligent-sounding, grammatically correct content and write code in response to a user prompt. Ecommerce websites rely heavily on sentiment analysis of the reviews and feedback from the users—was a review positive, negative, or neutral? Here, they need to know what was said and they also need to understand what was meant.

Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason. Latin, English, Spanish, and many other spoken Chat PG languages are all languages that evolved naturally over time. For more information on the applications of Natural Language Understanding, and to learn how you can leverage Algolia’s search and discovery APIs across your site or app, please contact our team of experts.

Constituency parsing combines words into phrases, while dependency parsing shows grammatical dependencies. NLP systems extract subject-verb-object relationships and noun phrases using parsing and grammatical analysis. Parsing and grammatical analysis help NLP grasp text structure and relationships.

Parsing establishes sentence hierarchy, while part-of-speech tagging categorizes words. Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers. With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5). As a result, they do not require both excellent NLU skills and intent recognition.

Our LENSai Complex Intelligence Technology platform leverages the power of our HYFT® framework to organize the entire biosphere as a multidimensional network of 660 million data objects. Our proprietary bioNLP framework then integrates unstructured data from text-based information sources to enrich the structured sequence data and metadata in the biosphere. The platform also leverages the latest development in LLMs to bridge the gap between syntax (sequences) and semantics (functions).

Their critical role is to process these documents correctly, ensuring that no sensitive information is accidentally shared. Let’s illustrate this example by using a famous NLP model called Google Translate. As seen in Figure 3, Google translates the Turkish proverb “Damlaya damlaya göl olur.” as “Drop by drop, it becomes a lake.” This is an exact word by word translation of the sentence.

nlu vs nlp

Natural Language Processing (NLP), a facet of Artificial Intelligence, facilitates machine interaction with these languages. NLP encompasses input generation, comprehension, and output generation, often interchangeably referred to as Natural Language Understanding (NLU). This exploration aims to elucidate the distinctions, delving into the intricacies of NLU vs NLP. As NLP algorithms become more sophisticated, chatbots and virtual assistants are providing seamless and natural interactions. Meanwhile, improving NLU capabilities enable voice assistants to understand user queries more accurately.

In essence, while NLP focuses on the mechanics of language processing, such as grammar and syntax, NLU delves deeper into the semantic meaning and context of language. You can foun additiona information about ai customer service and artificial intelligence and NLP. NLP is like teaching a computer to read and write, whereas NLU is like teaching it to understand and comprehend what it reads and writes. NLP is an interdisciplinary field that combines multiple techniques from linguistics, computer science, AI, and statistics to enable machines to understand, interpret, and generate human language. NLP relies on syntactic and structural analysis to understand the grammatical composition of texts and phrases. By focusing on surface-level inspection, NLP enables machines to identify the basic structure and constituent elements of language. This initial step facilitates subsequent processing and structural analysis, providing the foundation for the machine to comprehend and interact with the linguistic aspects of the input data.

The combination of NLP and NLU has revolutionized various applications, such as chatbots, voice assistants, sentiment analysis systems, and automated language translation. Chatbots powered by NLP and NLU can understand user intents, respond contextually, and provide personalized assistance. Natural Language Generation (NLG) is an essential component of Natural Language Processing (NLP) that complements the capabilities of natural language understanding.

Natural Language is an evolving linguistic system shaped by usage, as seen in languages like Latin, English, and Spanish. Conversely, constructed languages, exemplified by programming languages like C, Java, and Python, follow a deliberate development process. For machines to achieve autonomy, proficiency in natural languages is crucial.

NLU and NLP work together in synergy, with NLU providing the foundation for understanding language and NLP complementing it by offering capabilities like translation, summarization, and text generation. NLU seeks to identify the underlying intent or purpose behind a given piece of text or speech. It classifies the user’s intention, whether it is a request for information, a command, a question, or an expression of sentiment. Natural Language Processing (NLP) relies on semantic analysis to decipher text. NER systems scan input text and detect named entity words and phrases using various algorithms. In the statement “Apple Inc. is headquartered in Cupertino,” NER recognizes “Apple Inc.” as an entity and “Cupertino” as a location.

The Success of Any Natural Language Technology Depends on AI

Natural language processing is about processing natural language, or taking text and transforming it into pieces that are easier for computers to use. Some common NLP tasks are removing stop words, segmenting words, or splitting compound words. In this case, NLU can help the machine understand the contents of these posts, create customer service tickets, and route these tickets to the relevant departments. This intelligent robotic assistant can also learn from past customer conversations and use this information to improve future responses.

By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly. Machine learning, or ML, can take large amounts of text and learn patterns over time. The search-based approach uses a free text search bar for typing queries which are then matched to information in different databases. A key limitation of this approach is that it requires users to have enough information about the data to frame the right questions. The guided approach to NLQ addresses this limitation by adding capabilities that proactively guide users to structure their data questions using modeled questions, autocomplete suggestions, and other relevant filters and options. In human language processing, NLP and NLU, while visually resembling each other, serve distinct functions.

NLU & NLP: AI’s Game Changers in Customer Interaction – CMSWire

NLU & NLP: AI’s Game Changers in Customer Interaction.

Posted: Fri, 16 Feb 2024 08:00:00 GMT [source]

This helps in understanding the overall sentiment or opinion conveyed in the text. The procedure of determining mortgage rates is comparable to that of determining insurance risk. As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data. When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing.

While NLU focuses on interpreting human language, NLG takes structured and unstructured data and generates human-like language in response. NLU leverages machine learning algorithms to train models on labeled datasets. These models learn patterns and associations between words and their meanings, enabling accurate understanding and interpretation of human language. For machines, human language, also referred to as natural language, is how humans communicate—most often in the form of text. It comprises the majority of enterprise data and includes everything from text contained in email, to PDFs and other document types, chatbot dialog, social media, etc. Of course, there’s also the ever present question of what the difference is between natural language understanding and natural language processing, or NLP.

The future for language

Natural language understanding, also known as NLU, is a term that refers to how computers understand language spoken and written by people. Yes, that’s almost tautological, but it’s worth stating, because while the architecture of NLU is complex, and the results can be magical, the underlying goal of NLU is very clear. Harness the power of artificial intelligence and unlock new possibilities for growth and innovation. Our AI development services can help you build cutting-edge solutions tailored to your unique needs. Whether it’s NLP, NLU, or other AI technologies, our expert team is here to assist you. “I love eating ice cream” would be tokenized into [“I”, “love”, “eating”, “ice”, “cream”].

Where NLU focuses on transforming complex human languages into machine-understandable information, NLG, another subset of NLP, involves interpreting complex machine-readable data in natural human-like language. This typically involves a six-stage process flow that includes content analysis, data interpretation, information structuring, sentence aggregation, grammatical structuring, and language presentation. In 2022, ELIZA, an early natural language processing (NLP) system developed in 1966, won a Peabody Award for demonstrating that software could be used to create empathy.

Accurate language processing aids information extraction and sentiment analysis. NLU full form is Natural Language Understanding (NLU) is a crucial subset of Natural Language Processing (NLP) that focuses on teaching machines to comprehend and interpret human language in a meaningful way. Natural Language Understanding in AI goes beyond simply recognizing and processing text or speech; it aims to understand the meaning behind the words and extract the intended message. NLP consists of natural language generation (NLG) concepts and natural language understanding (NLU) to achieve human-like language processing. Until recently, the idea of a computer that can understand ordinary languages and hold a conversation with a human had seemed like science fiction. NLU delves into comprehensive analysis and deep semantic understanding to grasp the meaning, purpose, and context of text or voice data.

NLP centers on processing and manipulating language for machines to understand, interpret, and generate natural language, emphasizing human-computer interactions. Its core objective is furnishing computers with methods and algorithms for effective processing and modification of spoken or written language. NLP primarily handles fundamental functions such as Part-of-Speech (POS) tagging and tokenization, laying the groundwork for more advanced language-related tasks within the realm of human-machine communication. The future of NLU and NLP is promising, with advancements in AI and machine learning techniques enabling more accurate and sophisticated language understanding and processing.

People start asking questions about the pool, dinner service, towels, and other things as a result. Such tasks can be automated by an NLP-driven hospitality chatbot (see Figure 7). Sentiment analysis, thus NLU, can locate fraudulent reviews by identifying the text’s emotional character. For instance, inflated statements and an excessive amount of punctuation may indicate a fraudulent review. 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.

How do NLU and NLP interact?

NLG is used to generate a semantic understanding of the original document and create a summary through text abstraction or text extraction. In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content. Text abstraction, the original document is phrased in a linguistic way, text interpreted and described using new concepts, but the same information content is maintained. Modern NLP systems are powered by three distinct natural language technologies (NLT), NLP, NLU, and NLG.

Examining “NLU vs NLP” reveals key differences in four crucial areas, highlighting the nuanced disparities between these technologies in language interpretation. Integrating NLP and NLU with other AI domains, such as machine learning and computer vision, opens doors for advanced language translation, text summarization, and question-answering systems. Voice assistants equipped with these technologies can interpret voice commands and provide accurate and relevant responses.

Over 50 years later, human language technologies have evolved significantly beyond the basic pattern-matching and substitution methodologies that powered ELIZA. As we enter the new age of ChatGP, generative AI, and large language models (LLMs), here’s a quick primer on the key components — NLP, NLU (natural language understanding), and NLG (natural language generation), of NLP systems. The future of language processing and understanding with artificial intelligence is brimming with possibilities.

nlu vs nlp

Through the combination of these two components of NLP, it provides a comprehensive solution for language processing. It enables machines to understand, generate, and interact with human language, opening up possibilities for applications such as chatbots, virtual assistants, automated report generation, and more. Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), employs semantic analysis to derive meaning from textual content. NLU addresses the complexities of language, acknowledging that a single text or word may carry multiple meanings, and meaning can shift with context. Through computational techniques, NLU algorithms process text from diverse sources, ranging from basic sentence comprehension to nuanced interpretation of conversations. Its role extends to formatting text for machine readability, exemplified in tasks like extracting insights from social media posts.

NER systems are trained on vast datasets of named items in multiple contexts to identify similar entities in new text. While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities. One of the most common applications of NLP is in chatbots and virtual assistants. These systems use NLP to understand the user’s input and generate a response that is as close to human-like as possible. NLP is also used in sentiment analysis, which is the process of analyzing text to determine the writer’s attitude or emotional state.

It takes a combination of all these technologies to convert unstructured data into actionable information that can drive insights, decisions, and actions. According to Gartner ’s Hype Cycle for NLTs, there has been increasing adoption of a fourth category called natural language query (NLQ). NLP systems can extract subject-verb-object relationships, verb semantics, and text meaning from semantic analysis. Information extraction, question-answering, and sentiment analysis require this data. NLU is also utilized in sentiment analysis to gauge customer opinions, feedback, and emotions from text data.

By splitting text into smaller parts, following processing steps can treat each token separately, collecting valuable information and patterns. Language processing begins with tokenization, which breaks the input into smaller pieces. Tokens can be words, characters, or subwords, depending on the tokenization technique.

Artificial intelligence is critical to a machine’s ability to learn and process natural language. So, when building any program that works on your language data, it’s important to choose the right AI approach. In order for systems to transform data into knowledge and insight that businesses can use for decision-making, process efficiency and more, machines need a deep understanding of text, and therefore, of natural language. In machine learning (ML) jargon, the series of steps taken are called data pre-processing. The idea is to break down the natural language text into smaller and more manageable chunks. These can then be analyzed by ML algorithms to find relations, dependencies, and context among various chunks.

It involves various tasks such as entity recognition, named entity recognition, sentiment analysis, and language classification. NLU algorithms leverage techniques like semantic analysis, syntactic parsing, and machine learning to extract relevant information from text or speech data and infer the underlying meaning. By combining contextual understanding, intent recognition, entity recognition, and sentiment analysis, NLU enables machines to comprehend and interpret human language in a meaningful way.

As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills. NLU skills are necessary, though, if users’ sentiments vary significantly or if AI models are exposed to explaining the same concept in a variety of ways. However, NLU lets computers understand “emotions” and “real meanings” of the sentences. For those interested, here is our benchmarking on the top sentiment analysis tools in the market.

AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month. Bharat Saxena has over 15 years of experience in software nlu vs nlp product development, and has worked in various stages, from coding to managing a product. With BMC, he supports the AMI Ops Monitoring for Db2 product development team.

NLP full form is Natural Language Processing (NLP) is an exciting field that focuses on enabling computers to understand and interact with human language. It involves the development of algorithms and techniques that allow machines to read, interpret, and respond to text or speech in a way that resembles human comprehension. 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. NLG systems use a combination of machine learning and natural language processing techniques to generate text that is as close to human-like as possible.

Going back to our weather enquiry example, it is NLU which enables the machine to understand that those three different questions have the same underlying weather forecast query. After all, different sentences can mean the same thing, and, vice versa, the same words can mean different things depending on how they are used. Improvements in computing and machine learning have increased the power and capabilities of NLU over the past decade. We can expect over the next few years for NLU to become even more powerful and more integrated into software. Businesses can benefit from NLU and NLP by improving customer interactions, automating processes, gaining insights from textual data, and enhancing decision-making based on language-based analysis.

In this context, another term which is often used as a synonym is Natural Language Understanding (NLU).

In addition to processing natural language similarly to a human, NLG-trained machines are now able to generate new natural language text—as if written by another human. All this has sparked a lot of interest both from commercial adoption and academics, making NLP one of the most active research topics in AI today. In recent years, domain-specific biomedical language models have helped augment and expand the capabilities and scope of ontology-driven bioNLP applications in biomedical research. The collaboration between Natural Language Processing (NLP) and Natural Language Understanding (NLU) is a powerful force in the realm of language processing and artificial intelligence. By working together, NLP and NLU enhance each other’s capabilities, leading to more advanced and comprehensive language-based solutions.

The models examine context, previous messages, and user intent to provide logical, contextually relevant replies. NLP models can learn language recognition and interpretation from examples and data using machine learning. These models are trained on varied datasets with many language traits and patterns. One of the primary goals of NLP is to bridge the gap between human communication and computer understanding. By analyzing the structure and meaning of language, NLP aims to teach machines to process and interpret natural language in a way that captures its nuances and complexities.’s AI-Connect Blends NLP, NLU and LLM to Elevate Calling Experience – AiThority’s AI-Connect Blends NLP, NLU and LLM to Elevate Calling Experience.

Posted: Wed, 08 May 2024 14:24:00 GMT [source]

Additionally, it facilitates language understanding in voice-controlled devices, making them more intuitive and user-friendly. NLU is at the forefront of advancements in AI and has the potential to revolutionize areas such as customer service, personal assistants, content analysis, and more. Natural Language Generation(NLG) is a sub-component of Natural language processing that helps in generating the output in a natural language based on the input provided by the user. This component responds to the user in the same language in which the input was provided say the user asks something in English then the system will return the output in English. On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU. This enables machines to produce more accurate and appropriate responses during interactions.

NLU algorithms often operate on text that has already been standardized by text pre-processing steps. Natural language understanding is complicated, and seems like magic, because natural language is complicated. A clear example of this is the sentence “the trophy would not fit in the brown suitcase because it was too big.” You probably understood immediately what was too big, but this is really difficult for a computer.

As we embrace this future, responsible development and collaboration among academia, industry, and regulators are crucial for shaping the ethical and transparent use of language-based AI. To explore the exciting possibilities of AI and Machine Learning based on language, it’s important to grasp the basics of Natural Language Processing (NLP). It’s like taking the first step into a whole new world of language-based technology. By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Before booking a hotel, customers want to learn more about the potential accommodations.

The future of language processing holds immense potential for creating more intelligent and context-aware AI systems that will transform human-machine interactions. Contact Syndell, the top AI ML Development company, to work on your next big dream project, or contact us to hire our professional AI ML Developers. This allows computers to summarize content, translate, and respond to chatbots. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently.

  • These three areas are related to language-based technologies, but they serve different purposes.
  • Both NLP and NLU play crucial roles in developing applications and systems that can interact effectively with humans using natural language.
  • Machine learning, or ML, can take large amounts of text and learn patterns over time.
  • NLP, with its focus on language structure and statistical patterns, enables machines to analyze, manipulate, and generate human language.

The power of collaboration between NLP and NLU lies in their complementary strengths. While NLP focuses on language structures and patterns, NLU dives into the semantic understanding of language. Together, they create a robust framework for language processing, enabling machines to comprehend, generate, and interact with human language in a more natural and intelligent manner.

His current active areas of research are conversational AI and algorithmic bias in AI. Chrissy Kidd is a writer and editor who makes sense of theories and new developments in technology. Formerly the managing editor of BMC Blogs, you can reach her on LinkedIn or at The first successful attempt came out in 1966 in the form of the famous ELIZA program which was capable of carrying on a limited form of conversation with a user. All these sentences have the same underlying question, which is to enquire about today’s weather forecast.

It goes beyond the structural aspects and aims to comprehend the meaning, intent, and nuances behind human communication. NLU tasks involve entity recognition, intent recognition, sentiment analysis, and contextual understanding. By leveraging machine learning and semantic analysis techniques, NLU enables machines to grasp the intricacies of human language. The main objective of NLU is to enable machines to grasp the nuances of human language, including context, semantics, and intent.

  • NLP provides the foundation for NLU by extracting structural information from text or speech, while NLU enriches NLP by inferring meaning, context, and intentions.
  • In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content.
  • To explore the exciting possibilities of AI and Machine Learning based on language, it’s important to grasp the basics of Natural Language Processing (NLP).
  • By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8).
  • It provides the ability to give instructions to machines in a more easy and efficient manner.
  • As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data.

But this is a problem for machines—any algorithm will need the input to be in a set format, and these three sentences vary in their structure and format. And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly. NLP employs both rule-based systems and statistical models to analyze and generate text. Linguistic patterns and norms guide rule-based approaches, where experts manually craft rules for handling language components like syntax and grammar. NLP’s dual approach blends human-crafted rules with data-driven techniques to comprehend and generate text effectively.

Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. Natural Language Processing(NLP) is a subset of Artificial intelligence which involves communication between a human and a machine using a natural language than a coded or byte language. It provides the ability to give instructions to machines in a more easy and efficient manner. The two most common approaches are machine learning and symbolic or knowledge-based AI, but organizations are increasingly using a hybrid approach to take advantage of the best capabilities that each has to offer. For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc.

Sentiment analysis systems benefit from NLU’s ability to extract emotions and sentiments expressed in text, leading to more accurate sentiment classification. The algorithms utilized in NLG play a vital role in ensuring the generation of coherent and meaningful language. They analyze the underlying data, determine the appropriate structure and flow of the text, select suitable words and phrases, and maintain consistency throughout the generated content. NLU goes beyond literal interpretation and involves understanding implicit information and drawing inferences. It takes into account the broader context and prior knowledge to comprehend the meaning behind the ambiguous or indirect language. Information retrieval, question-answering systems, sentiment analysis, and text summarization utilise NER-extracted data.

NLU is widely used in virtual assistants, chatbots, and customer support systems. NLP finds applications in machine translation, text analysis, sentiment analysis, and document classification, among others. Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time. This is in contrast to NLU, which applies grammar rules (among other techniques) to “understand” the meaning conveyed in the text. Sometimes you may have too many lines of text data, and you have time scarcity to handle all that data.

These examples are a small percentage of all the uses for natural language understanding. Anything you can think of where you could benefit from understanding what natural language is communicating is likely a domain for NLU. Knowledge-Enhanced biomedical language models have proven to be more effective at knowledge-intensive BioNLP tasks than generic LLMs. In 2020, researchers created the Biomedical Language Understanding and Reasoning Benchmark (BLURB), a comprehensive benchmark and leaderboard to accelerate the development of biomedical NLP.

A test developed by Alan Turing in the 1950s, which pits humans against the machine. 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. Businesses like restaurants, hotels, and retail stores use tickets for customers to report problems with services or products they’ve purchased. For example, a restaurant receives a lot of customer feedback on its social media pages and email, relating to things such as the cleanliness of the facilities, the food quality, or the convenience of booking a table online.

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