Understanding Semantic Textual Similarity
Artificial Intelligence is a technology that has seen huge strides in its development over the past few years. This 2018, companies like Google have been developing the technology to improve user-friendliness and allow more functionality in the services that they offer, such as Google Assistant, Google Search, and much more.
These new developments have been headed by Google AI, which is their main research team handling AI technology development. This recently-announced division of Google has been developing an improved AI system, which they demonstrated in May 2018. The demonstration saw Google Assistant’s capability to make phone calls and respond naturally to conversational speech. This capability showcased how Google Assistant is able to handle tasks without the need for user supervision.
Google and other tech giants are currently developing their own sophisticated AI, systems, and these breakthroughs show that AI technology still has long ways to go, and new algorithms and features have yet to be refined for release. One of the latest findings from Google AI is the new Semantic Textual Similarity algorithm, which expands the ability for AI to understand various queries and interactions to create more diverse responses. Let’s take a look at these new updates and see how they can change things up for the future of search.
What is Semantic Textual Similarity?
To set things straight from the get-go, Learning Semantic Textual Similarity is a new algorithm that enables AI system to be able to understand semantically similar queries from users and provide a uniform response. This algorithm aims to not only improve the quality of responses by services such as Google Assistant but also make these interactions feel more natural as well.
For example, if the user asks “How old are you?” or “What is your age?”, the user will expect the same response. The emphasis on semantic similarity aims to create a system that recognizes language and word patterns to craft responses that are similar to how a human conversation works. This algorithm is accomplished through a series of processes and systems that connect together to create an AI system that understands speech patterns. These processes are classified as models, and here are some of the ones that are used by Google.
Universal Sentence Encoder
To create authentic and natural response patterns, Google AI uses a system called a Universal Sentence Encoder that predicts sentences through the use of encode-only architecture that helps drive more predictive tasks that recognized text and creates responses. The encoder performs tasks such as custom text clarification, paraphrase detection, and clustering. This system works by using a deep average network encoder, which is an arrangement that is simpler and more straightforward in generating results. Meanwhile, another version of the Universal Sentence Encoder uses a more complicated network system called the Transformer, which is able to perform better and create quality results.
How will this affect SEO?
The rise of AI assistants and mobile internet also saw the increase in the number of users using voice search on a regular basis. This also meant that applications that use voice search must be able to recognize a diverse array of words and sentence patterns to generate accurate responses. This quality is what makes AI Assistants like Google Assistant, Siri, Alexa, and Bixby some of the most widely used applications in the market, and can be accessed through mobile applications, laptops, and even smart furniture.
With this in mind, the best SEO strategies that allow you to take advantage of voice search is using exact and phrased search terms, long-tail keywords, and creating pages that answer commonly asked questions. Mobile SEO will also become a big part in the future of search as well, especially with more users browsing through mobile, and Google adopting a mobile-first approach.
Voice search optimization is quickly becoming an important practice in the industry, and it is best to implement these on your websites to future-proof your SEO and allow AI assistants to find your website much easier. Mobile optimization is another important matter, as mobile loading speed will not become an important ranking factor this 2018. Creating a user-friendly website through AMP and responsive design helps make the experience of navigating your website much better.
AI technology may still be a growing technology that requires a lot of refinements and optimization for it to develop further, but what we currently have shows how much potential that it has in revolutionizing the SEO industry, and how users do search inquiries. Semantic Textual Similarity is just one of these ingredients that help make it work better, and one day allow AI assistants to have more autonomy in the tasks that they accomplish.
If you have questions and inquiries about AI technology and SEO, leave a comment below and let’s talk.
Original source: https://seo-hacker.com/semantic-textual-similarity/