In 2019, Google announced BERT Search Engine Algorithm Update, what they call as the most important algorithmic occurrence after 2015’s RankBrain. And as expected, it has a far-reaching impact on how searches are comprehended and responded by the Google search engine.
BERT expands to Bidirectional Encoder Representations from Transformers. As per the experts from Google, it is a core algorithm update that aims at improving the understanding and interpretation of language, knowing the nuances and contexts of the search queries.
In the easiest words, BERT is about improving the language understanding capabilities of the search engine.
The BERT update by Google has helped the engine to understand the context of the search better. Instead of processing each word in the phrase separately, BERT takes each word in a single query and assigns them a token. Also, it reads the query bidirectionally, feeds on semantics, rules out contextually irrelevant queries, and offers weights to different words based on their role in the phrase as per NLP (Natural Language Processing) technology. This helps the search engine render more relevant and accurate results.
Refer to two phrases ‘nine to five’ and ‘quarter to five’. Here the word ‘to’ has two different meanings which are understandable by humans but least comprehended by humanoids and machines. BERT is the attempt to work on such language nuances of a search query. To which, BERT is meant to bring out results that are highly relevant and appropriate to the user intent of the search.
BERT trains on language models. It feeds on language sequences to learn the contexts. Building an understanding of the entire set of words in a sentence.
It goes bi-directionally to interpret the meaning now, unlike the traditional method of reading the query in a sequence of left-to-right or combined left-to-right and right-to-left. Google calls it deeply bidirectional. As it aims at establishing the context going deep in the neural networks and pattern recognition techniques.
Taking an example, if you talk about the word ‘bank’ it has different meanings in the phrases ‘bank account’ and ‘river bank’. Before BERT was introduced you could get overlapping and contextually irrelevant result for both the key phrases used.
BERT plays around contextual models and refers to the other words in the sentence or phrase. It builds the depth of understanding of the phrases using the ever-advanced bidirectional training approach marked as Masked LM. This helps it identify the underlying idea or meaning of the word by putting it together with the whole discourse or affiliated expression of the phrase suggested by the order of speech, linguistic references, phrasing (including stop-words), and the combination of words that go along.
So, if there is a search query ‘register a bank account’, the unidirectional model would have just thrown results picking up the word bank and it could even go on to include the results related to river banks or blood banks, before BERT. But not anymore, as BERT stresses understanding the context by going through the whole query and interpreting its contextual meaning. So, when it reads the word account and register, it rejects the possibility of it being related to a riverbank or a blood bank. And this goes deep and sharp in segregating the meanings and the ideas behind the expressions the search queries hold. And therefore, it connects the meaning of the word ‘bank’ with a financial institution and delivers related results. This is how BERT’s application in searches impact results.
BERT is an open-source model that is an extension to Google AutoML Natural Language to explore better possibilities at contextually interpreting languages. The idea goes on to dominate the way human languages are interpreted and the meaning behind those is contemplated and understood by the machines. This is the concept that works behind BERT NLP that is meant to optimize Natural Language Processing in the upcoming years.
Google BERT Search Algorithm Update has a great impact on optimizing the results for conversational queries (the queries that humans speak out or text conversationally to a machine). Particularly for the longer queries that involve natural human language that is dominated by the use of prepositions like ‘to’ and ‘for’ or the use of fuller expressions innate to human speeches.
BERT implements the ‘feature-based’ and ‘fine-tuning’ language representation strategies to alleviate the earlier unidirectional approach by attributing the queries with the “masked language model” (MLM). With this technique, Google BERT masks word semantics randomly to pick the context of the search phrases. It does this by interpreting the whole conversations bidirectionally, referring to the co-existence roles and discourses of keywords, performing sentence-level and token-level tasks. This is how it has improved the way conversational queries work.
There’s no scheme or strategy you need to follow to optimize your content for BERT. Just that, you need to stick to the universal policy of keeping the user intent in mind while framing your content. Keep in mind how you would communicate to your user in general and keep your context covered. That’s it!
And that’s the only advice you get from the experts at Google. Optimizing your content for BERT and even for any such future update, you need to focus on good writing that is user-friendly and contextually relevant and do away with the flat, keyword-beefed and cluttered communication that is constructed for engines.
Google BERT Search Algorithm Update is a great step towards optimizing the search engine results, understanding the natural language pattern and user intent of the search. It is about knowing the user context better than ever and getting the experience with engine searching relevant, valid, and all the more ‘natural’. Marking the timeline of Google’s futuristic updates for more evolved and human-sensitive engines that know people and their ideas better!