Understanding Semantic Nearest Neighbor Theory
Dawn Anderson shares her expertise in semantic relationships of content for the EDGE audience in this second part of her interview with Erin Sparks. They dive into the machine learning concepts that are being honed right now for search engines to better understand the relationship between topics and the method of semantic distancing. Search machine learning is sorting out what is valuable and relevant to the user in ways it has never done before, and Dawn Anderson weighs in on whether data science should be a place for the SEOs of the world to focus on – get a chuckle at timestamp: [00:39:04]!
- [00:01:40]
- [00:04:44] What is Next in Semantic Search?
- [00:07:09] The Dense Retrieval Process
- [00:08:35] Pulling Hard Negatives to Speed Up Search
- [00:12:25] Search Result Diversification is Expensive
- [00:13:14]
- [00:13:13] The Research is Open Source
- [00:16:06] Walnuts and Peanuts: It’s All About Importance
- [00:18:43] It’s Not the Amount of Content; it’s the Completeness of the Niche Topic
- [00:20:33] What is Semantic Distancing?
- [00:24:50] You’ve Got to Know More About Their Industry Than They Do
- [00:28:01] Is That Page Good Enough to Represent the Query?
- [00:31:20] Finding the Content Gap in Client’s Industry
- [00:33:27] Blogs are Jumble Sales!
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