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Paste a public article URL to compare Google Cloud NLP’s entities with the topic map TopicalBoost prepares for editorial review. Try a few articles free. No signup.

Analyzed article
topicalboost.com
Checking Google’s Work: How TopicalBoost Corrects NLP
+5topics surfaced

What Google missed

TopicalBoost surfaced five relevant topics missing from Google’s entity baseline, then separated the article’s focus from mentions.

What Google Detected

Entities returned by Google Cloud NLP.

Google Jarno van Driel Natural language processing Large language model John Mueller Search Engine Land Google Search PageRank

TopicalBoost Topic Map

Prepared for review: article focus separated from topics mentioned in passing.

About
Google Natural language processing Large language model
Mentions
Jarno van Driel John Mueller Search Engine Land Google Search PageRank Artificial intelligence Search engine optimization Schema.org Knowledge graph Structured data
How this comparison works

This is a real analysis of the linked article. Google Cloud NLP returned 24 entities; TopicalBoost surfaced five topics Google missed. The comparison shows representative retained entities and all five additions. Google returns entities and salience scores; TopicalBoost prepares a topic map for editorial review, organized into About and Mentions.

This real analysis surfaced 5 relevant topics beyond Google’s baseline. What could TopicalBoost surface across your archive?See the CMS workflow, structured data, and internal-linking tools in a short walkthrough.

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