Understanding NLP is Important for Anyone Managing a Business Online
Most businesses online today are still operating on the principles of early 2000s era of search engine optimisation. To understand the shift from SEO to discoverability stack, it is important to understand how natural language processing actually works as well as it’s subsets like Natural language understanding (NLU), Natural language generation (NLG) and Natural language querying (NLQ). This will enable a business to strategically structure their site’s content as well as entire digital footprint, make it digestible by AI engines and dominate the web traffic.
Natural Language Processing serves as a layer between unstructured human data and structured machine intelligence. In present day discoverability stack which includes generative engine optimisation as well, we are moving away from keywords to entities and relationships.
The AI powered chatbots understand user’s intent based on the information they have. These cues could include location, phrasing of prompts, past behaviour etc. Here is where the contextual decoding happens. After understanding the content, comes synthesis. In this step AI agents would read say ten contextually appropriate pages and then generate an answer for the user.
Natural Language Understanding
Natural Language Understanding is a subset of NLP that focuses on comprehension of human language. It helps machines decipher the meaning behind natural language text.
It includes aspects like
- Entity recognition i.e. understanding names, dates and locations within a sentence.
- Intent recognition i.e. what are the possible meanings which the user could be implying.
- Ambiguity resolution, English is a funny language, what one human says or writes can be interpreted in many ways by others. Hence ambiguity resolution forms an important part of NLU. Example - I saw North Koreans with a Telescope. (Who has the telescope here, the person or the North Koreans?)
Role of NLU in Discoverability
NLU helps to a great extent in discoverability of your content and it’s context for the AI models. NLU looks for “entities”. If you understand this, you will stop old school keyword stuffing and start building knowledge graphs within your content. Your focus will shift towards how concepts relate to each other establishing the intended context.
For example, it will help the AI engine understand that a page about “Apple” is actually about the technology company and not the fruit based on surrounding words like “Silicon”, “Operating system”, “MacBook”, “Device”. This help answer engines to understand the user query and provide content from the most authoritative documents available on the web.
Read: Knowledge Graphs - Brain Behind the shift from SEO to Discoverability
Natural Language Generation
While NLU was all about reading and comprehension, Natural language generation is about generation of text i.e. writing. This is the process where machine understandable structured data is converted into human readable text.
We have come a long way in NLG. From templatised responses to well written prose, poetry, technically strong articles, creative story writing to even computer code.
Your Site’s Content is the Key to be Cited by AI Answer Engines
AI engines use your data for it’s own training purposes and for generation of textual answers users request. If an LLM based chatbots like Gemini or one of it’s application like Notebook LM summarises your article, it first understands it contextually. If your writing is ambiguous, then the LLM may hallucinate or misinterpret your data. It may potentially skip it altogether.
When it comes to discoverability, one must optimise content for “AI citations”. The goal is that your content, key points and statistics mentioned are structured and clearly explained so that AI can extract them into it’s generated answer whenever required.
Natural Language Querying
Natural Language Querying is a bridge between human language and the computer database. It enables users to ask questions in human language, English or any other for which the model is trained and query the database. When we say database, it could mean your excel file or even the entire web.
ChatGPT can understand and generate 95+ languages. Performance remains best for major languages like English, Spanish, Chinese, Hindi even.
NLQ replaces the task of writing complex codes like SQL. Example, if human uploads a table and says, “Show me the top three selling products in category A from the month of September”, NLQ will translate and query it like
SELECT product_name
FROM Sales
WHERE category = 'A' AND month = 'September'
ORDER BY units_sold DESC
LIMIT 3;
Concepts of Natural Language Querying Apply when users are trying to find your business online.
Let’s assume you are a cab service provider. When a user puts a prompt into an LLM based AI chatbot like Gemini or Perplexity, he or she would say - ‘Find me an out station cab service from Delhi to Dehradun’. Here the AI chatbot will try to query the web to find a the relevant cab service. The result it comes up with actually depend on the semantic architecture of your website and service listing. If your services are structured enough for it to understand, it will pull it up, if not then you are as good as invisible.
Hence, it is important to have semantic architecture. An agent can understand your services via NLU and recommend you during an NLQ session.
Natural Language Interaction
This reference is seldom used but explains the full loop of communication. NLU understands the user input, NLG responds to the user by giving the requested output. They together provide a conversational experience which is very close to human interaction. This has found applications in voice assistants or customer service agents and chatbots.
Natural Language Reasoning
Natural Language Reasoning is machine’s ability to apply logic to the actions it takes and the content it produces. NLR forms the crux of NLP and why it is so effective today. Machines are now able to draw conclusions from what the humans enter as input.
Machine Translation
A major subset and an application of NLP is machine translation. It is dedicated to translating text to speech OR content (text / speech) from one language to another. MT maintains the tone and context while translation. Earlier, it used to be word for word translation (Neural machine translation - NMT) but now it has evolved into maintaining tone and context as well. They look at sentence as a whole and do the meaningful translations.
The role of Natural Language Processing (NLP) has shifted from being a "behind-the-scenes" technology to the primary filter through which the world accesses information. In the current landscape of the web, machines no longer just index keywords; they attempt to understand the world conceptually and contextually.