In this post, I’ll provide a foundational overview of NLP, explain its process, and give a brief on the BERT and MUM updates. I’ve also included step-by-step instructions on how to use the Google NLP API demo along with another tool to better understand entity relationships in your content. You’ll find the tool links below, and I’ve also included a video walkthrough that demonstrates how these tools work in real time.
If you have any questions or need further clarification, feel free to drop a comment or send me a message!
Natural Language Processing – It’s a machine learning technology that understands the data/information to know how reliable it is and how it relates to natural language.
Every time we conduct a search on a search engine result page (SERP), Google interprets and processes our query across multiple languages and intents, displaying the results in a matter of seconds. Let’s understand the complete process to know how this algorithm works.
Google integrated into search engines, and it is known as BERT. Google NLP algorithms apply at scale across languages & domains to improve search results in mobile apps, ads, translation, and more. This was not the first time Google invented such an NLP model; there are more others that have been invented by Google, such as the SMITH and LaMDA AI language models.
BERT—bidirectional encoder representations from transformers. It follows Google’s own NLP model based on neural network architecture. That means it is not looking for individual content phrases but instead tries to find out the context of the sentences to determine if it’s better than the results already ranking in the top positions.
NLP API comprises five different services
- Syntax analysis – Parts of Speech and Dependency trees
- Sentiment Analysis – Prevailing Emotion opinion score
- Entity Analysis – Person, location, date, year, organization, etc
- Entity Sentiment Analysis – Relation between the entities
- Text Classification – Understand the content to define its various categories
MUM—Multitask Unified Model—Released in 2021
Whereas MUM is known as 1000 times more powerful than BERT, it is an advanced version of BERT for quality voice, video, and image search algorithms. Also for improved NLP as compared to BERT. MUM is designed to handle more complex queries.
- MUM is to be trained in up to 75 languages simultaneously to understand them. With previous methods, each language was trained in its own language model.
- Google uses MUM to further expand the semantic databases, such as the Knowledge Graph, and to come closer to the goal of a complete knowledge database.
Tools
https://cloud.google.com/natural-language
Reference:
digitalguider[dot]com/blog/what-is-google-nlp/
searchengineland[dot]com/how-google-uses-nlp-to-better-understand-search-queries-content-387340
analyticsvidhya[dot]com/blog/2021/04/how-to-use-googles-nlp-api-to-analyze-and-produce-better-content/
