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Let's Learn to Communicate in BERT

For natural language processing (NLP), BERT is an open-source machine learning framework. The BERT method uses surrounding text to establish context for ambiguous language in the text. Pre-trained with Wikipedia text, BERT can be fine-tuned with question-and-answer datasets.


(Source: SEO.SRL)


So what is BERT?

BERT stands for Bidirectional Encoder Representations. It is a language model published by Transformers in 2018 that achieved state-of-the-art performance on numerous tasks, including question-answering and language comprehension.


What makes BERT different?

Earlier, language models could read the text either left-to-right or right-to-left but never simultaneously. Transformers uniquely created BERT as a deep learning model that understands the text in layers. It connects each element and each input individually to decode the text. BERT can read in both directions simultaneously. This ability is also known as 'bi-directionality'.


BERT is pre-trained on two independent but related NLP tasks using this bidirectional capability: Masked Language Modelling and Next Sentence Prediction.


  • Masked Language Modelling: helps the program to identify a hidden word based on the sentence shared.

  • Next Sentence Prediction: empowers BERT to recognise whether two given sentences have a logical, sequential connection or whether their relationship is random.



(Source: Pinimg)


Why must we use BERT?

BERT is controlled by Google's main algorithm Hummingbird, like RankBrain. Here are a few reasons why BERT is most relevant for Google,

  • It can be used for large amounts of data

  • It looks for the context of the word.

  • It helps Google to be more quality driven, easily highlighting misleading information

  • BERT is open-source, which makes it accessible


What does this mean for digital marketing and SEO?


BERT is empowering Google, and its accuracy is improving each day. Hence, while publishing or posting content, advertisers must maintain the tone, context and emotion. It is also crucial to not hide any subtext in the content that will deviate from the key points. In addition, BERT's accuracy makes it easy to identify keyword stuffing.


While BERT has improved the accuracy of Google indexing and crawlers, advertisers need to be smart while choosing keywords. Here's what they can do,

  • Avoid keyword stuffing

  • Use relevant keywords

  • Create content with consistent subtext and sentiment

  • Do not include misleading words in your sentences

  • Do not create content that will indicate a wrong hidden word or message