Saijo George

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tuesday2 Jun 2020

Google Was Recently Granted a Patent on Using Neural Network Models for Understanding Text and Semantic Data

Bill Slawski breaks down a newly granted Google patent related to using neural network models for understanding text and semantic data. This is not to be confused with the Google Patents Context on Vectors to Improve Search – that tells us that when a word has more than one meaning, Google might look at other terms on a page to try to get a better sense of the meaning of that word on that page.

In this new patent however they are trying to understand the meaning of a word when used in a particular context. Because many words have different meanings when used in different contexts, the system disambiguates the possible word senses for a word using the context for the word, i.e., the other words in the text sequence identified in the request, to select the appropriate word sense to return in response to a received request. For example, when used in the sequence “I went fishing for bass,” the word “bass” has the sense “fish,” while when used in the sequence “My friend plays the bass,” the word “bass” has the sense “musical instrument.”


Writing naturally appears to be the best way to optimize so that Google can understand the word sense being used by the choice of a word that might have multiple word senses. If you are writing about fishing for bass, your choices of words in what you write will be very different than when you may write about performing a song with a bass stringed musical instrument.

Make sure when you write about a word that may have multiple senses that you use context terms that help define the meaning of that word.


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