Natural Language Processing (NLP) is now an essential part of how content is understood and ranked organically. SEOs need to grasp this concept to ensure they not only follow changes in search but also stay ahead of the curve. Search engines like Google are continuously increasing their reliance on NLP to understand the intent behind searches so they can be better equipped to serve up the best and most helpful results to its users.
NLP is driving many of the changes SEOs need to consider when creating content, structuring site architecture, understanding user intent, and more.
What is NLP in SEO?
Natural language processing is a branch of linguistics and artificial intelligence that bridges the gap between computers and human language. This involves teaching computers and machines to interpret and generate useful and meaningful responses in human language.
Put simply, NLP gives computers the power to process and analyze large amounts of data and text by extracting necessary insights and generating something a human can use and interpret without the need for follow-up questioning.
How Does NLP Work?
While natural language processing is a complex topic, we can break it down into a few different steps. The entire process involves a number of techniques and algorithms to bridge the gap between human language and machine understanding. The infographic below summarizes and simplifies these six steps:
A deeper breakdown of the process reveals a little more of what’s going on under the hood of the NLP car. There are specific actions that have to take place before a response is generated, most of which happen within seconds.
Here’s a breakdown of some of the steps happening when search engines utilize natural language processing:
1. Text Preprocessing: Text processing involves tokenization, breaking down text into individual words and removal of common words like “the,” “and,” “is,” or words that don’t add meaning to the request. During this process, the machine will tag parts of the speech to identify the role of important words to improve its understanding. These tags will identify whether the query is intended to answer a question, address an assertion or parse out a collection of keywords.
2. Feature Extractions: Feature extraction represents the conversion of text and audio data into numeric data that computers can easily understand. Some machine techniques included in this stage are TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings like Word2Vec or GloVe.
3. Machine Learning Models: The machine learning model stage applies common processing models so the machine can understand and process the data more accurately. Some of the models include:
- Naive Bayes: Classification tasks like sentiment analysis.
- Support Vector Machine (SVM): Text classification and categorization.
- Hidden Markov Model (HMM): Speech recognition.
- Deep Learning: This includes Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), and Transformers (BERT) used for translation, text generation, and question answering.
4. Natural Language Understanding (NLU): This step extracts meaning and intent from the text the machine has already converted. This includes entity recognition and relationship understanding.
5. Natural Language Generation (NLG): The final step is to create human-like text based on the data the machine was able to gather, convert, and understand.
6 Ways To Use NLP for Modern SEO
1. Prioritize User Search Intent
The natural language understanding (NLU) portion of the language processing steps is arguably the most important. Search engines seek clarity, this is why structured data has been on the rise for some time now. The more clarity a search engine has on the concepts and text you include in an article or with your voice, the better they can formulate a response.
The focus on clarity of content shouldn’t just be done for the benefit of the search engines’ algorithm; instead, it’s a bridge between technical optimization and customer needs. Create content that is tailored to your intended customer persona by answering queries geared toward four different user intents:
- Informational Intent: Seeking answers to specific questions. These can often be answered in the SERP through various SERP Features such as AI Overview. By prioritizing informational content, you may increase visibility but decrease traffic.
- Navigational Intent: Looking for a specific site or login page.
- Commercial Intent: Intending to buy a product or service. The search is likely a comparison between products.
- Transactional Intent: Expecting to complete a transaction such as going through the motions of actually purchasing or booking a service.
2. Write For Clarity
Step out of the content brief for a second and write your blog post like you’re talking with your best friend. Too often SEOs are sucked into the black hole of content briefs, SEO alignment, and self-consciousness about what Google will think about their writing. Since the introduction and widespread use of AI-generated content, real content writing has never been more valuable.
Even as you’re reading this, you can tell that it wasn’t written by AI. Clarity is the most important part of communication. We need to focus more on how people learn and actually understand the concepts we chose to write about in the name of generating more organic traffic.
8 Tips for Clear Writing
- Avoid complex sentences.
- Vary sentence length and style.
- Use an active voice.
- Use precise language instead of vague statements or jargon.
- Choose strong verbs to convey meaning.
- Break up long blocks of paragraphs into bite-sized chunks.
- Maintain a consistent tone throughout your writing.
- Avoid phrases and words that are clear AI writing signals (even if this is your usual way of writing), i.e.: “AI-driven,” “ever evolving technology in an AI-driven world.”
3. Include Related Entities
Related entities are critical for improving your NLP SEO. Including them in your content helps search engines get a better understanding of the context and depth of your content.
Including related topics helps search engines categorize your content so it can match it to relevant queries. Covering a topic holistically means including entities. It not only helps with categorization, but it also demonstrates expertise on the subject. Including entities means that you understand how concepts are connected and what information is needed to deliver a comprehensive understanding to the user.
From a keyword perspective, they can also carry their own value. Related entities are often long-tailed keywords that help expand the reach of your content, assist in answer engine recognition, and attract more targeted traffic. All of these things lead up to more engaged users who are more likely to stick around and learn, improving session duration and the likelihood of conversion.
4. Double Down on Semantic Keyword Inclusion
On a similar thread, semantic keyword inclusion improves a search engine’s understanding of your topic. In the early days of SEO where a targeted keyword in an H1 and title tag would get you to the top of search results, semantic SEO was just another way of expanding your organic visibility and showing your expertise.
Focusing your efforts on semantic keyword inclusion gives you a competitive advantage and sets your content apart from the competition by connecting semantic topics to high priority keywords. This way, you’re naturally writing from a user-centric point of view. It’s important to remember that you don’t want to overuse or insert these keywords where they don’t naturally flow with the rest of the content. The focus should remain on creating high-quality content that informs the user and genuinely answers their questions.
5. Use Question-Answer Formats
Question-answer format is a great way to quickly educate users on a concept while also ensuring complete coverage of a topic. Providing question-answer formatted content in your marketing efforts can help search engines get a better view of your own understanding of the concepts you’re talking about.
Not only is the question-answer format good to include for NLP SEO, it’s also a good way to get listed in AI Overviews and to practice answer engine optimization (AEO). NLP is a key component of answer engines since it prioritizes user-centered language.
This format can take place either inside your content or in an FAQ format at the end of a blog post or landing page. However, it’s important to recognize that moving important questions to the FAQs may impact your visibility on LLMs for that topic since adding content to the end of a page can affect some crawlers’ ability to effectively capture all your content. This is especially relevant with LLMs.
6. Intentionally Structure Your Content
While the substance of your content is essential, thoughtful structure serves as an invisible guide, leading readers effortlessly through your post. You want to make sure you’re providing a pathway for the user to digest the information. When structuring any blog post, follow a logical progression that builds understanding. Begin by defining your core concept, then explain fundamental principles, and outline practical applications.
Content structure also assists with user engagement metrics. Give the user what they want, when they need it. If your content is complex and difficult to understand, make sure to lead the reader into the concept by using clear definitions and simple explanations to frame your content.
Keep It Natural
Creating content for natural language processing should be, well, natural. You don’t want to try to complicate things with industry jargon. You want to create a connection between you and the user. Make them feel like they’re talking to a friend. This will not only help you engage more users, but search engines and answer engines are also more likely to understand and choose to serve your content in key ranking areas.
Our expert team of SEOs and growth experts can help you transform your SEO strategy into a winning playbook of evergreen content quickly. If you want to learn how, talk to us!
