Google Search Algorithms Use Big Data for Multilingual Latent Semantic Indexing

Google is successful in the English-speaking world, but it has a way to go yet with some other languages. It's using semantic indexing to help it get there.

June 21, 2018
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Google has perfected its ability to execute web search results for its users all over the world. In the early days of the Internet, the search engine was primarily suited for displaying search results for English users. Non-English-speaking users have complained that search results are often displayed in the wrong language entirely. However, Google is becoming more proficient at providing search results in other languages as well. A lot of factors can play a role, but one of the biggest is its use of deep learning to understand semantic references—enter semantic indexing. This can now be accomplished in any language that Google serves.

Semantic Indexing is Google’s Newest Advantage in the Search Engine Market

Google has dominated the search engine industry for nearly two decades. The search engine giant has thrived by refining its algorithm to better infer user intent and match people with the most relevant content. Over the past few years, they have perfected this outcome by using deep learning to better understand the context of search queries their customers are using.

Of course, human technicians do not provide search results for the 3.5 billion searches on Google every day. The search engine aggregates content based on a ranking system dependent solely on artificial intelligence. Such an AI system would be rather simple if there were a finite number of pre-defined inputs.

Of course, that obviously is not the case. Human beings who use Google to conduct search queries are notoriously unpredictable. They can invent an endless number of search queries. In fact, 15% of search engine queries have never been used before.  The spectrum of search terms is changing all the time to reflect new trends in the macro environment.

How Can Google Account for This Unpredictable Behavior?

In order to handle increasing volume of search queries, Google had to become very adaptable about being able to understand the true meaning of different search queries. This required the algorithms to understand the contextual meaning behind various word pairs, rather than individual words without any contextual markers.

Deep learning has played a crucial role in this process. Google web crawlers have scanned the Internet to understand the relationship between various words in specific contexts. The more frequently these pages are indexed, the better understanding the algorithms have of the relationship between various words.

Opportunities and limitations of using deep learning and semantic indexing for aggregating multilingual search results

Google has captured over 70% of the global search engine market. However, it does not have close to a monopoly in some regions. In fact, in some parts of the world, less than 1% of all search careers are conducted through Google. Native search engines are more dependable for indexing relevant content for users speaking those languages than Google. Some of this discrepancy is due to regulatory policies in authoritarian regimes, but it is also partially due to Google’s limited ability to understand the contextual meanings of various search phrases and languages other than English.

According to Shout Agency, an SEO agency in Australia, the structure of the algorithms themselves is not the core problem. Google can index any content in any language and make educated assumptions about relevance based on its own knowledge of various word pairs. While Google developers have intentionally built in biases for some search phrases, such as the “payday loan” penalty, these adjustments are the exception rather than the rule.

So, if the algorithms are equally suited for aggregating search results in any language, why is there a discrepancy in the quality of search results in different languages? The problem almost entirely stems from the fact that Google has had fewer opportunities to conduct deep learning in some languages than others. There is less content available and fewer users are searching in those languages.

Over time, though, the results will improve. As long as more content is created, web crawlers will have more opportunities to understand the nature of different search terms and aggregate content appropriately.

However, there is one risk that needs to be considered. Google is less likely to conduct manual penalties for content in some regions, due to the smaller user base and fewer Google employees that can understand the language enough to gauge the quality of the content. This could mean that there is going to be a greater prevalence of spun content, which will likely throw off the results of the algorithms that depend on deep learning.

However, this is unlikely to be an issue in regions with popular languages, such as Spanish, Portuguese, and French. Deep learning will continue to improve the quality of search results in almost every language across the world.