Enhancing the Performance of Medical Search Queries on Google
Late last year, Google started rolling out what has been touted as the most significant leap in search since the introduction of RankBrain five years ago. Known as BERT – Bidirectional Encoder Representations from Transformers – the new NLP framework is set to significantly enhance the performance of global search.
Understanding the Role of BERT in Medical Content
To truly understand BERT and how it impacts search, we first need to understand the wider discipline of Natural Language Processing (NLP). Melding elements of computer science, artificial intelligence, and linguistics, NLP is the field focusing on teaching machines how human language works, or training computers to understand and recognize the nuances of human language.
‘Deep’ NLP as we know it emerged in the early 2010s, and today we see it applied in many aspects of everyday life – from online chatbots, to predictive text messages, to trending topics on Twitter, to voice assistants like Apple’s Siri, Amazon’s Alexa, and Google Assistant.
NLP goes beyond training machines to understand spelling and grammar, it also involves teaching machines to understand the definitions of a word in different contexts. NLP is used to help computers recognize and distinguish between definitions based on the context of the overall input. It’s also used to help computers recognize the tone or sentiment behind a piece of text or a word. A great example of this is how tools like Grammarly can identify whether the tone of a passage is optimistic, aggressive, formal, neutral, etc.
What is BERT then?
What BERT achieves is quite simple: it uses a number of innovative mechanisms and processes in order to understand human language better than any other NLP framework has ever been able to achieve.
BERT is taught a general understanding of how language works using a massive corpus of text data, and then this general knowledge can be fine-tuned for any specific language-related problem you might have.
Prior to being rolled out in search, BERT had already achieved state-of-the-art results for 11 different natural language processing tasks. If, for example, you wanted to create a chatbot for your pharmaceutical company, you could take BERT’s pre-trained architecture and fine-tune it for this specific task and your specific products and customers. You could input a dataset containing thousands of product reviews, each tagged ‘positive’ or ‘negative’, and further train BERT in sentiment analysis to understand how to distinguish between future positive and negative reviews.
Benefits of Recent BERT Updates for Medical Search Queries
Google is now using BERT to improve search results. While BERT can be applied to several NLP tasks, this update specifically pertains to search queries, and to helping Google fully understand the true intent of a query.
“How to test your own blood sugar”
“Test blood sugar”
Here, we have two different ways someone might ask what seems to be the same query. But they’re not the exact same query – and there are significant distinctions between them that, with BERT, search engines can now distinguish.
While results for ‘test blood sugar’ may present a broad scope of solutions, a search for ’how to test your own blood sugar’ would serve results specifically relating to DIY solutions. Before BERT, the search engine likely wouldn’t have recognized the importance of the words ‘your’ and ‘own’ in that search, and would’ve provided broad results either way.
Now, BERT can build a representation of the meaning for both the entire query and for each word simultaneously. The model is able to recognize all of the ways that each word may interact and, using bidirectional Transformers, can determine the true intent of the query, and subsequently provide the most relevant results.
Currently, 10% of Google searches in the U.S. use BERT to serve the most relevant results – typically on “longer, more conversational queries”. BERT is also currently only trained for the English language. While there is no defined timeline, Google are committed to expanding the update to both a larger percentage of queries and to more languages in the future.
What does BERT mean for users?
For users on Google, BERT means improved search query results, and therefore an enhanced user experience. As the BERT algorithm continues to develop and as Google continues to roll out the update, the search engine’s understanding of human language will continue to improve considerably. Search results will become more relevant and responsive, and better served for your specific needs. It will become easier and easier to find the information you need.
BERT is also used for Google’s featured snippets, again providing more relevant, accurate results. It is likely you’ll begin to notice these improvements in featured snippets like Answer Boxes and ‘People Also Ask’ lists.
What impact does BERT have on Medical SEO?
You cannot optimize for BERT, so the only way for SEO to really leverage this update is to ensure that their content is always focused on the audience and their needs.
BERT is not a ranking tool, and it doesn’t assign values to pages; it is simply used so Google can better understand the intent of the user.
As search engines push towards a more human way of understanding queries, so too should the content people are searching for. The more focused your content is on the specific intent of the user, the more likely it is that BERT will recognize this connection. Understand your audience, what they search for and how they search for it; less keyword stuffing and more natural, human content is key.What impact does BERT have on Medical SEO? You cannot optimize for BERT. The only way for SEO to leverage this update is to ensure that content is always focused on the audience's needs. Find out more-> https://bit.ly/2AZ2v1C Click To Tweet
How will BERT impact medical translation?
While current BERT models concentrate only on English, as it develops it will become hugely useful for machine translation (MT). If BERT can learn the nuances of English, then it can do so for any language, and in time we will very likely see BERT or new natural language processing models built upon BERT’s architecture greatly improve the accuracy and performance of MT.
A system like BERT is capable of learning from the English language and applying these learnings to other languages. Already, Google’s BERT algorithm is being used to improve featured snippets in 24 countries, and this has seen improvements in languages such as Korean, Portuguese, and Hindi.
Engaging with BERT as it continues to develop allows digital marketing experts at Welocalize Life Sciences the opportunity to anticipate and subsequently capitalize upon these innovations for global brands driving multilingual SEO campaigns within the ever-evolving search environment.
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Author: This article was written by Michael de Alwis. It is an abridged version of a longer article that appears in full on the Adapt Worldwide website here.