How NLP is used in Search


Today, websites are at the mercy of SEO which dictates what content search engines can rank in result pages for specific keywords.


SEO refers to search engine optimization. It measures how worthy a website and its content are of ranking higher in a search engine.


Google, the world’s largest search engine, uses algorithms (the secret formula) to measure the success of your SEO efforts. Over time, it has incorporated numerous changes in its algorithms


Google’s algorithms are the key to SEO. 


By the end of 2019, Google released its latest BART algorithm, which weighs on natural language processing to understand the context of a searcher’s query.


It’s definitely a game-changer for SEO.


But how?


According to Google, the BART algorithm is different from traditional search engines and understands a user’s query by focusing more on the context. This means it can help content creators who build in-depth, more specific, and relevant content and links rank higher in the result pages.


Google has introduced natural language processing (NLP) in the form of BART as a powerful tool for businesses to draw insights from data sets and analysis. 


So, NLP is one factor that you need to account for as you plan to improve SEO efforts and your SEO strategy.


If you want to kickstart your efforts to manage your ranking in Google, you must learn how Google uses NLP and how it helps you improve your rank in search engine results pages (SERPs).


The Evolving Role of Natural Language Processing 

Natural language processing is a machine’s ability to comprehend human language identical to how humans do.


It helps make text understandable to machines and adds semantic annotations to content to describe it accurately.  


To understand the role of NLP in search, you first need to learn how BERT works.


BERT refers to Bidirectional Encoder Representations from Transformers.


It is a combination of two components: data (which refers to pre-trained models) and methodology (which defines how to learn and use models.)


Let’s put it this way, BERT collects data sets or content related to specific keywords and learns how the data can be analyzed.


NLP functions as the brain of BERT. It helps understand keywords by looking at the context in which they are used. From succeeding to preceding words and from one section of a page to an entire page, BERT uses natural language processing to analyze content.


Natural language processing is not a new technology that Google has adopted all of a sudden. It has been around for decades and is used by various search engines.


But now, it is the foundation of the BERT algorithm. 


Primarily, NLP is Google’s way of understanding language (content) better. 


When it comes to optimizing content for search engines, NLP has three main tasks to perform- text recognition, text comprehension, and text generation.


Text Recognition

Computers understand the language of numbers, not the text. So, natural language processing performs text conversion (to numbers) so that computers can understand it.


Text Comprehension

Not just conversion, NLP also helps algorithms perform statistical analysis of the web content to discover the phrases and words that frequently appear together. As a result, algorithms identify keyword volume.


Text Generation

NLP machines provide content creators with questions and answers around which they can create their content.


Additionally, NLP and AI advancements are able to help writers generate content in a short time as writers only need to put together keywords and central ideas. Then, NLP applications take care of the rest.


As NLP keeps evolving, writers may also eventually have machines to handle much of their SEO-related work.


How Natural Language Processing Can Help Improve SEO

Understanding how the BERT algorithm functions and uses NLP can help you fine-tune your SEO strategy


Let’s learn what areas of search this algorithm improves: 


Core Understanding Of Search Intent

If you want to rank higher in SERPs, you need to keep an eye on how Google updates its algorithms to improve search quality.


For Google, what matters most is the search intent of the user. 


Today’s users are smarter, specific about what they need, as well as impatient. So, Google keeps updating algorithms and introducing better tools to understand searcher’s intent before any other search engine does.


Google states that 15% of its daily search queries are new and used for the first time, which means users are using long-tail keywords and longer descriptions to search every day. 


It makes Google’s job even tougher to make sense of the searcher’s intent because algorithms do not always have historical data to comprehend unique queries.


However, it is critical for the search engine to understand the query and its context. To make this task simpler, the search engine uses natural language processing. 


NLP essentially helps Google understand the queries and analyzes content based on sentiment, category, salience, and relevance to an individual query.


Sentiment Importance

To understand the context, Google has developed a factor, sentiment, to comprehend the content, which certainly works with the help of Google speech to text.


In layman’s terms, sentiment is the tone or the choice of words presented in the content. Sentiment can be “positive, negative, or neutral.”


Positive sentiments in the content are given a score between 0.25 and 1. On the flip side, the negative sentiment score is -0.25 and -1.0. 


Now, we are left with the neutral sentiment which is scored between the numbers -0.25 and +0.25.


Why is sentiment important?

The answer is simple:


For example, there are 100 pages that seem relevant to a query, and 99 present a positive sentiment. But, your page is mostly classified as having negative sentiment. So, you will get a score between -0.25 and -1.0. 


Based on sentiment score, there are strong chances that Google will not consider your page relevant to what people are searching for.


The Entity, Salience, and Category

When you want to understand NLP and how it works in search, it is vital for you to learn these three terms.


Entity– it represents words, people, objects, etc. that search engines can identify, classify, and categorize.


Category– nothing much to explain it. We have overly heard this term in SEO and are familiar (or we assume you are) with how Google uses categories to categorize content for SERPs. 


Salience– refers to the importance of an entity in content. For an entity, the salience score ranges from 0.0 to 1.0.o


Some entities are more important than others in specific contexts. In order to understand their importance, Google uses NLP to analyze words or phrases that surround them.


The importance of entities determines their relevance to a query. 


For example, when the query is about “breakfast”, the entity “morning” would be more important and more relevant than “evening”. So, the pages that talk about morning and breakfast will be seen as more relevant to the query and searcher’s intent.


So, the more relevant an entity is to the query, the higher the salience score. Higher relevance and the salience score increase the ranking possibility of that page.


Final Thoughts

NLP in SEO is important now and in the future as it helps improve a searcher’s experience with the content and a site’s ranking by determining the relevancy of content.


To improve the ranking of your site, as a website owner, you need to make sure your content presents positive sentiment, includes relevant entities (keywords), and answers the question people are asking.


By keeping your content as relevant to a query as possible, you can qualify for the algorithm to consider your site important and rank you in SERPs.