A notebook containing code to help with reorganizing your content in order to optimize it for BERT to achieve better SEO results.
- The purpose of this code is to test Bidirectional Encoder Representations of Transformers (BERT) in order to understand how BERT answers questions given context.
- Furthermore, we can see how the use of embedding helps with analyzing the similarity of words with and without context.
- Using the text from your webpage as context, the code uses BERT in order to answer any given question pertaining to the text.
- The output contains the answer, the level of confidence (score), and the starting and ending indices in the source context.
- Given a list of words or sentences, we can view the level of similarity between the words, with or without context.
- You can use this code in order to optimize your content for search questions using deep learning.
- Since Bing and other major search engines are using BERT, you can use the script for SEO on your web pages containing the most valuable information.
- By using frequently asked questions by your users, you can test whether BERT can extract the best answers from your page.
- If you run the script on your page and a competing page, you can compare the scores and reorganize the content on your page to optimize it for BERT and gain a competitive edge.
Visit the notebook