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The BERT update is an advanced algorithmic update that helps Google understand natural language in a more in-depth manner, especially in conversational searches and complicated search queries that depend on the context of results. It is said that the BERT update can impact over 10% of search queries.

In this article, we have discussed all you need to know about this latest algorithmic update by Google, like what it actually is, how it works, its impact on search, SEO and content marketing, and how it will help users.

What is BERT and How It Actually Works?

“BERT” is the abbreviation for “Bidirectional Encoder Representations from Transformers”. The four letters, B.E.R.T is actually a lot more than being a simple algorithmic update. It is an open-source research project and academic paper which was first published in October 2018. BERT has also been reckoned to be a very potent machine learning natural language processing framework.

The key objective of the Google search algorithm is to help Google better understand search queries, the nuances and context of the searched words, which can consequently help Google provide users with more relevant and useful results.

The natural language processing NLP framework was open-sourced by Google to enable the entire natural language processing research field to get a better grasp on language understanding. When it comes to how BERT works, or what it actually does, it is a big step forward to the biggest challenge which is the fact that a lot of contexts and interpretations are understood by humans very easily but machines are not able to understand such things.

BERT helps address some of the key areas associated with the use and choice of words, their most appropriate meanings and relevance. When it comes to language understanding, BERT helps in a few key areas that are:

• Ambiguity
• Polysemy
• Synonyms
• Multiple meanings
• Ambiguous sentences
• Homophones and prosody
• Contextual understanding of words and sentences

Words are problematic for machines to understand. For instance, let’s take the example of the word, “like”. The word can be used in different parts of speech like verb, noun, as well as an adjective. The word in itself has no meaning, and the meaning can change with whatever surrounds the word. The context of the word changes according to the meanings of the words that surround it.

That being said, every word in a sentence gets its meaning from the context of the entire sentence and the longer the sentence is, the more difficult it becomes to keep track of the different parts of speech within the particular sentence.

BERT: The Natural Language Tasks It Performs

The BERT algorithm utilises multiple language processing techniques to get a better grasp of the context and the content and deliver more relevant results.

Natural Language Recognition (NLR) and National Language Understanding (NLU)

NLU basically involves a combination of common sense reasoning and understanding of context. A lot of structured data can help simplify the NLU process but not everything can be mapped into the black and white area or the knowledge graph. There is a lot of grey area in between the data available, which is better understood and interpreted with the help of the BERT algorithm.

BERT works through the process of Natural Language Disambiguation. This means that NLP models learn and understand the weights of the similarities and relatedness of words in a certain context. It works through the simplification of many scenarios like:

• Co-occurrence
• Co-occurrence leads to context.
• Co-occurrence that alters the meaning of words
• Strongly connected words
• Similarity and relatedness

How BERT Helps Users

BERT makes use of language models which are developed and trained on large text corpora. These are large collections of words that help learn distributional similarity. While previous language models were uni-directional, “B” in BERT stands for “Bi-Directional”, while “ER” is “Encoder Representations” which means that anything that is encoded is decoded, while “T” means “Transformers”, a masked language modelling.

When it comes to how BERT helps users, these are a few natural language tasks it can perform:

• Determining of Named Entities
• Next Sentence Prediction
• Solving Co-references
• Answering Question
• Disambiguation of Word Sense
• Summarising automatically
• Resolving polysemy

With these, it helps in:

• Scaling Conversational Search
• Better Understanding Human Language
• Understanding Contextual Nuances
• Resolving Ambiguous Queries

Impact of BERT on Search, SEO and Content Marketing

BERT has brought about huge transformations in the entire Google Search field and has made a major impact on the ways and techniques of SEO and content marketing. Be it the multi-linguistic ability which helps a lot of patterns in a certain language easily translates into other languages.

Google BERT majorly affects the field of SEO and content marketing in the following ways:

• Major impact on Top-Of-The-Funnel keywords.
• Quality of the content becomes more improved than its length.
• Keyword Density becomes less important.
• Long-Tail Keywords become more important.

Final Words

Ever since its implementation in October 2019, BERT has induced a soar in activities related to production search. There has been a major shift in the field of content and the way language processing works. With the BERT update, users can look forward to more relevant and precise search results in the near future.