1. Overview
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are both critical research topics in the Natural Language Processing (NLP) field. However, NLU is to extract the core semantic meaning from the given utterances, while NLG is the opposite, of which the goal is to construct corresponding sentences based on the given semantics. In addition, NLP allows the use and understanding of human languages by computers.
In this tutorial, we’ll present the difference between NLP, NLU, and NLG using some examples.
2. What Is NLP?
NLP refers to the field of study that involves the interaction between computers and human language. It focuses on the development of algorithms and models that enable computers to understand, interpret, and manipulate natural language data.
Let’s now present the relationship between NLP, NLG, and NLU, along with the fundamental elements of each:
2.1. Text Classification
Text classification is the process of categorizing textual data into predefined classes or categories. Here are some examples:
- Classifying customer reviews as positive or negative sentiment based on the sentiment expressed in the text
- Classifying news articles as sports, politics, or entertainment based on their content
- Determining the language in which a given text is written, such as English, Spanish, or French
2.2. Information Extraction
Information extraction is the task of identifying and extracting structured information from unstructured text. Here are some examples:
- Extracting names, dates, and locations to create a structured database of events
- Extracting phone numbers, email addresses, and addresses from a resume to populate a job application form
- Extracting information about specific events or incidents mentioned in the text
2.3. Machine Translation
Machine translation is the process of automatically translating text from one language to another using computational algorithms. Here’s an example:
- Input: “I’m going to the cinema”
- Output: “Je vais au cinéma”
 2.4. Question Answering System
A question answering system is designed to automatically provide answers to user queries based on the information available. Here’s an example: answering questions like “What is the capital of France?” with the response “Paris.”
However, NLP techniques aim to bridge the gap between human language and machine language, enabling computers to process and analyze textual data in a meaningful way.
3. What Is NLU?
NLU is a subfield of NLP. It’s concerned with the ability of computers to comprehend and extract meaning from human language. It involves developing systems and models that can accurately interpret and understand the intentions, entities, context, and sentiment expressed in text or speech. However, NLU techniques employ methods such as syntactic parsing, semantic analysis, named entity recognition, and sentiment analysis.
3.1. Syntactic Parsing
Syntactic parsing is the process of analyzing the grammatical structure of a sentence to identify the relationships between words and their roles in the sentence, let’s present some examples:
- For example, determining that in the sentence “The cat chased the mouse,” “cat” is the subject and “chased” is the verb
- For example, identifying the subject, verb, and object in the sentence “John eats an apple.”
- Input: “The cat chased the mouse.”
- Output: Root: chased, Dependencies:
, 
, 
, 
3.2. Semantic Analysis
Semantic analysis is the process of understanding the meaning and interpretation of the text by considering the context and relationships between words. It refers to the process of comprehending the meaning of text beyond its literal interpretation. Here are some examples:
- Interpreting that “She loves dogs” implies a positive sentiment toward dogs
- Understanding that the phrase “hot as fire” implies extreme heat rather than actual flames
- Input: “John eats an apple.”
- Output: Predicate: eat, Arguments: Agent: John, Patient: apple
3.3. Named Entity Recognition (NER)
NER is the task of identifying and classifying named entities, such as names of people, organizations, locations, dates, and other specific entities within the text. Here are some examples:
- Identifying “Apple” as an organization in the sentence “Apple Inc. is a technology company.”
- Recognizing “New York” as a location in the sentence “I visited New York last summer.”
- Input: “I live in San Francisco and work for Google.”
- Output: [LOCATION: San Francisco], [ORGANIZATION: Google]
3.4. Sentiment Analysis
Sentiment analysis is the process of determining the emotional tone or sentiment expressed in a piece of text:
- Identifying whether a tweet expresses joy, sadness, or anger
- Determining whether a movie review expresses a positive or negative opinion
- Assisting in market research by analyzing customer opinions on new products, campaigns, or market trends, helping companies make informed business decisions
- Input: “I absolutely loved the movie! The acting was superb, and the plot kept me engaged throughout.”
- Output: Positive sentiment
4. What Is NLG?
NLG is a subfield of NLP that focuses on the generation of human-like language by computers. NLG systems take structured data or information as input and generate coherent and contextually relevant natural language output. NLG is employed in various applications such as chatbots, automated report generation, summarization systems, and content creation. NLG algorithms employ techniques, to convert structured data into natural language narratives.
4.1. Text Planning
Text planning is a stage in NLG where the structure, order, and coherence of the generated text are determined. Here are some examples:
- Organizing the introduction, main points, and conclusion of a persuasive essay to create a well-structured and cohesive narrative
- Arranging the steps in a recipe in a clear and sequential order to guide the reader through the cooking process
4.2. Data-To-Text Transformation
Data-to-text transformation involves converting structured data into natural language narratives. Here are some examples:
- Generating a weather report by transforming weather data, such as temperature, humidity, and precipitation, into a human-readable description of the current weather conditions
- Generating a sales report by transforming sales figures, trends, and analysis into a narrative summary of the company’s performance
4.3. Surface Realization
Surface realization is the process of generating the final form of the text, including grammar, word choice, and linguistic variations. Let’s take a look at some examples:
- Transforming a structured representation like “I will go to the store” into the surface form “I’m going to the store.”
- Converting a logical form like “John eats apples” into the surface form “John is eating apples.”
5. NLP vs. NLU vs. NLG
Let’s take a look at the difference between NLP, NLU, and NLG:
- NLP plays a crucial role in bridging the gap between human language and machines
- Its importance lies in enabling machines to understand, interpret, and generate human language, facilitating effective communication and interaction with users
- Natural Language Understanding involves the comprehension of language input, enabling machines to understand and interpret the meaning and intent behind text or speech
- NLG focuses on generating human-like language, allowing computers to produce coherent and contextually appropriate text or speech output
- These three components form the fundamental building blocks for developing intelligent language-based systems, such as chatbots, virtual assistants, and automated content-generation tools
- As a result, NLU enables meaningful interpretation, NLP processes and analyzes language data and NLG facilitates the generation of language output
In this table, we’ll present the distinctions between NLP, NLG, and NLU based on their respective focuses and objectives:
6. Conclusion
In conclusion, NLP, NLU, and NLG play vital roles in the realm of artificial intelligence and language-based applications. Therefore, NLP encompasses both NLU and NLG, focusing on the interaction between computers and human language.