How to Optimize Your Content for Search Questions using Deep Learning
One of the most exciting developments is how well Bing and other major search engines can answer questions typed by users in search boxes.
Search is moving farther and farther away from the old ten blue links and matching typed keywords with keywords in content.
Answers to search questions can come up in the form of intelligent answers where we get a single result with the answer, and/or “People Also Ask”, where we get a list of related questions and answers to explore further.
This opens both opportunities and challenges for content producers and SEOs.
First, there is no keyword mapping to do as questions rarely include the same words as their corresponding answers. We also have the challenge that questions and answers can be phrased in many different ways.
How do we make sure our content is selected when our target customers search for answers we can provide?
I think one approach to do this is to evaluate our content by following the same process that Bing’s answering engine follows and contrast it to an evaluation of competitors that are doing really well.
In the process of doing these competitive evaluations we will learn about the strengths and weaknesses of the systems, and the building blocks that help search engines answer questions.
BERT – Bidirectional Encoder Representations of Transformers
As stated by Jeffrey Zhu, Program Manager of the Bing Platform in the article Bing delivers its largest improvement in search experience using Azure GPUs:
“Recently, there was a breakthrough in natural language understanding with a type of model called transformers (as popularized by Bidirectional Encoder Representations from Transformers, BERT) … Starting from April of this year, we used large transformer models to deliver the largest quality improvements to our Bing customers in the past year. For example, in the query "what can aggravate a concussion", the word "aggravate" indicates the user wants to learn about actions to be taken after a concussion and not about causes or symptoms. Our search powered by these models can now understand the user intent and deliver a more useful result. More importantly, these models are now applied to every Bing search query globally making Bing results more relevant and intelligent.”
Source: Bing Blog Feed