How does SEOQuantum extract text from a page?
By developing a solution inspired by this Why use our content approach, we would be able to create a scalable tool that stands the test of time. So we decided to approach the netherlands telegram data task using machine learning.
We collected a large number of web pages to create a dataset. From this dataset, we trained a neural network to extract content information.
We trained this tool to analyze several elements of each text block (such as its size, location on the web page, text size, text density, etc.) as well as the entire page image (e.g., visual layout and features that humans and Google consider).
We now have a reliable tool that can predict the likelihood that a given block of text will be considered part of the web page content.
Aggregation of multiple content sections
We then had to aggregate the results of each block of Why use our content text into a score that took into account the weightings relative to the importance of each block.
There are two different ways to do this:
- Calculate the weighted averages of all passages and then measure their distances from the target keyword
- Calculate the distance between each block and the keyword, then take a weighted average of all distances.
These two options do not measure the same thing. The first measures the distance between the “average content” and the search term, while the second measures the average distance between each piece of content and the keyword.
Imagine that we have extracted four blocks of text from a web page which, once integrated into our vector space.
The first method is to calculate the average position of all embedded content and then measure it against the search term depth:
The second method is to calculate the distance of a niche site like this could feature each block from the search term . We then average all these different lengths, which gives a different result. In this example, the average distance length is much greater with the second method than with the first.
Step 2 – Measure the relevance of the content?
The first method attempts to aggregate all Why use our content of the page’s content into a single point, regardless of how diverse the individual content blocks are relative to each other, while the second penalizes content that is diverse and less focused on the search term (even if the aggregation of that content is very close to the search term). Since each method yields a different result in terms of distance, which one should we use?
The first method is closest to what we want to measure, which is the overall relevance of the content. The second method is to look at the degree of content accuracy, which can also be useful. We decided that our content relevance tool (the tool’s Semantic Score) should measure both of these things and chose to use the first method to measure overall relevance and the second to measure content accuracy.
By using normalization concepts to Why use our content convert distances into percentages (where 100% represents zero distance and 0% represents infinitely large distance), we can measure both the relevance distance and the accuracy distance of a web page’s content.
Having two scores allows us to assess the usefulness of cn leads content on a case-by-case basis, taking into account the topic and search terms. While it would make sense to aim for two scores as high as possible, a precision score that is too high isn’t always necessary. The precision of content will vary depending on the user’s intent. For example, a user Why use our content searching for the phrase “Yoga” might be looking for information about Why use our content Yoga classes and the benefits of Yoga , in which case the content they want to read isn’t restricted.