Would you believe us if we said NLP, NLU, and NLG are already a part of your daily life and probably part of your business as well?

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These acronyms stand for "natural language processing," "natural language understanding," and "natural language generation," respectively, and they're all used within the context of AI (Artificial Intelligence) technology that you encounter daily.

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These processes are used everywhere: in the results of your online searches, in voice assistants like Amazon’s Alexa and Apple’s Siri, and in chatbot conversations that offer a personal assistant to help answer questions. And it’s a strong ally for businesses that need to respond to a variety of customers, all at once, with personalized information.

What is natural language processing?

Natural language processing (NLP) is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language in a way that is both natural and meaningful. NLP involves the interaction between computers and humans using natural language. It encompasses a wide range of tasks, including:

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1. Text understanding. This involves tasks such as parsing, sentiment analysis, named entity recognition, and text classification. The goal is to extract meaning from text data.
2. Text generation. NLP can also be used to generate human-like text, such as in chatbots, language translation, and summarization systems.
3. Speech recognition. NLP techniques are applied to convert spoken language into text, enabling systems like virtual assistants to understand and respond to spoken commands.
4. Language translation. NLP is used extensively in machine translation systems, allowing the translation of text from one language to another.
5. Question answering. NLP techniques are applied to understand questions posed in natural language and provide accurate answers from a given dataset or knowledge base.

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NLP systems typically involve techniques from computational linguistics, machine learning, and deep learning, among other disciplines. These systems learn from large amounts of annotated data and are trained to perform specific tasks related to understanding and generating natural language.

What is natural language understanding?

Natural language understanding (NLU) is a subfield of NLP that focuses on the comprehension of human language by computers. While NLP broadly covers the entire spectrum of tasks related to processing natural language, NLU specifically deals with the understanding aspect.

Here's a breakdown of what AI text analysis with NLU involves:

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• Semantic understanding. NLU is used to extract meaning from text or speech. This includes tasks such as identifying entities (people, places, organizations), recognizing relationships between entities, understanding sentiment, and grasping the overall context of a piece of text.
• Syntactic analysis. NLU involves analyzing the structure of sentences to understand the relationships between words, phrases, and clauses. This includes tasks such as parsing sentences to identify grammatical structure, understanding word order, and recognizing parts of speech.
• Pragmatic understanding. NLU considers the pragmatic aspects of language, such as understanding implied meaning, recognizing metaphors, and interpreting context-dependent language.
• Disambiguation. NLU systems must be able to recognize and resolve ambiguities in language. This includes clarifying between words with multiple meanings, interpreting pronouns correctly, and understanding the intended meaning of ambiguous phrases.
• Contextual understanding. These systems aim to understand language in context. This involves considering the broader context of a conversation or document to accurately interpret meaning and make informed decisions.

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NLU is a crucial component of many applications, including virtual AI assistants, chatbots, sentiment analysis systems, machine translation, and information retrieval systems. This is the type of technology that would be valuable to a college professor interested in using an AI content detector tool to scan students' work. It's looking for patterns in language that indicate a student has used an AI generator to instantly produce an essay they waited until 11:30 p.m. to start.

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By enabling computers to understand human language at a deeper level, NLU systems can provide more natural and effective interactions between humans and machines, and even help combat AI misuse.

What is natural language generation?

Natural language generation (NLG) is another key aspect of NLP, focusing on the generation of human-like text or speech by computers. Unlike NLU, which involves extracting meaning from human language, NLG involves the generation of language output.

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Here's a look at what NLG entails:

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• Content planning. NLG systems typically start by planning the content they need to generate. This may involve selecting relevant information from a database, organizing the information into a coherent structure, and determining the main points to convey in the generated text.
• Text structuring. NLG systems then structure the generated text according to linguistic conventions. This involves organizing sentences and paragraphs in a logical order, providing coherence and cohesion, and applying appropriate style.
• Linguistic realization. NLG systems convert the planned content into natural language text or speech. This involves selecting words and phrases, inflecting verbs, conjugating nouns, and applying grammatical rules to generate grammatically correct and fluent output.
• Stylistic variation. NLG systems may also incorporate stylistic variation to produce text with different tones, registers, or styles.
• Personalization. Some NLG systems can personalize generated content based on individual preferences or characteristics. This may involve incorporating user-specific information, adapting the language to match the user's profile, or tailoring the content to suit the user's needs or interests. For example, they may generate formal language for in-house business memos, conversational language for chatbots, or persuasive language for marketing materials.

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But how can you best make use of these new tools?

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What are some examples of natural language generation?

NLG has plenty of applications, including:

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• automated report generation
• automated chatbots
• chats with virtual assistants
• content generation for websites and marketing materials
• language translation
• accessibility tools for generating alternative formats of text or speech.

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By enabling computers to generate human-like language, NLG systems can automate the creation of content, streamline communication processes, and enhance user experiences in human-computer interaction

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When Siri, Alexa, or Cortana answer your questions, they’re using Natural Language Generation and other programming to translate text into a spoken form.

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When “Sam, your personal assistant” responds to your written query (perhaps as you’re waiting for a live person to respond), the company's software is using chatbot technology to interpret what you have typed and then to respond with an appropriate message through NLG technology. The software searches for keywords in your questions, and then uses specific applications to generate pre-written answers based on the frequency of their usage.

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That’s a lot to consider, sure, but there’s an easy way to understand the distinctions between these various forms of AI. It’s so much more than Robotic Processing Automation, a form of business process automation technology used to do repetitive, low-value work.

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Thanks to the data scientists who’ve done all the research and much of the work for us, NLG is revolutionary for marketers hoping to personalize responses using natural language to clients.

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You may have heard of (or used) one of the most prolific generators of AI content, ChatGPT. OpenAI, a San Francisco-based research lab, created GPT-4, the latest version. The most sophisticated NLG model, GPT-4, or Generative Pre-trained Transformer 4, can write poetry, prose, and even computer coding that is hard to distinguish from that created by humans. The fourth version of this AI tool is 10 times more advanced than GPT-3. It has huge improvements in data processing speed, language comprehension, and its ability to process visual and audio input.

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How can businesses use NLP, NLU, and NLG for marketing?

We are likely to see more and more Natural Language Processing as AI technology is integrated into every aspect of marketing and business. Businesses in many sectors benefit from the sophistication that NLP, NLU, and NLG allow them to offer clients and customers:

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NLP in content marketing

This is one of Rellify's specialties. Its state-of-the-art platform uses NLP in all three of these aspects of your content pipeline, ensuring the best and most efficient use of this technology.

1. Competitive analysis. NLP can analyze competitors' content to identify gaps, uncover emerging trends, and benchmark performance. This insight can inform content strategy and help businesses differentiate their offerings.
2. Content optimization. NLP systems can optimize content for search engines by analyzing keyword usage, readability, and semantic relevance. This includes tasks such as keyword extraction, entity recognition, and sentiment analysis to ensure that content aligns with SEO best practices.
3. Content creation. NLP can assist in generating content ideas by analyzing trending topics, keyword research, and competitor analysis. It can also automate tasks such as content summarization, topic clustering, and content curation, helping marketers identify relevant content opportunities.

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NLU in content marketing

This branch of NLP focuses on the nuances of understanding human language.

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1. Audience understanding. NLU enables marketers to better understand their target audience by analyzing social media conversations, customer feedback, and online reviews. By understanding audience sentiments, preferences, and pain points, marketers can create more relevant and engaging content.
2. Content personalization. NLU techniques can personalize content for different audience segments based on their interests, demographics, and behavior. This includes dynamically generating content variations, such as email subject lines, article headlines, and product recommendations, to resonate with specific audiences.
3. Content distribution. NLU can optimize content distribution strategies by analyzing audience engagement metrics and identifying the most effective channels and timing for content delivery. This helps marketers reach their target audience more effectively and maximize the impact of their content.

NLG in content marketing

This is the aspect of NLP we're most familiar with — the conversational chatbots and ChatGPTs. But it's so much more than just a fun tool, it's a game-changer for online marketing.

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1. Content generation. NLG can automate the creation of various content formats, including blog posts, articles, social media posts, and product descriptions. Marketers can use NLG to scale content production, generate content variations, and maintain consistency across channels.
2. Email marketing. NLG can personalize email marketing campaigns by generating personalized email subject lines, body copy, and product recommendations. This helps marketers increase open rates, click-through rates, and conversions by delivering more relevant and engaging content to subscribers.
3. Content localization. NLG can assist in translating and localizing content for different markets and languages. By generating localized versions of content, marketers can expand their reach and connect with global audiences more effectively.

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By leveraging NLP, NLU, and NLG in content marketing, businesses can create more impactful content, better understand their audience, and drive engagement and conversions across various digital channels.

Put NLP, NLU, and NLG to work for your content marketing

Rellify has strategically leveraged these types of AI within all aspects of the content marketing pipeline, from keyword and topic research to content generation and monitoring. With the latest offering — a custom Relliverse, you can use NLP to crawl large volumes of content throughout the web, and use its expertise to find topics and keywords that best match your target customers' niche search engine queries. Then, you'll be ready to knock your competition out of the water with relevant, optimized, and well-written (or generated) content.

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Contact a Rellify expert today to find out how it uses Intelligence Augmentation, machine learning, and NLP to create uniquely optimized content that naturally ranks high in search engine results. We'll even give you a free demo of the platform and the unparalleled capabilities of a custom Relliverse. What are you waiting for? Revolutionize your content strategy and processes today!