1β£ The latest 10 articles I wrote on the blog.
2β£ My entity page (as *system message*).
3β£The article the user is reading.
4β£A combination of the two indices above (a graph index).
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Readers can now π¬ AskMe any question they like π.
With this article I wanted to demonstrate the potential of an hybrid approach that combines natural language understanding, knowledge graphs and language models.
I worked on my first experiment with ChatGPT Plugins applied to SEO! I created a search engine for an eCommerce site using WordLift's Product Knowledge Graph (PKG) as a starting point. Here is what I learned:
I have worked on automatic image captioning since 2019; recently, I have done a new analysis of the SOTA.
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I am generally less interested in models and more interested in finding the framework to fine-tune and evaluate models, so I found LAVIS by @salesforce, which provides access to 30 pre-trained and task-specific fine-tuned models checkpoints (ALBEF, BLIP, CLIP, and ALPRO).
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A small thread about Semantic SEO and #ChatGPT πͺ‘
As @SilviaKaya22 was testing @wordliftit new version of the SEO Add-on for Google Sheetsβ’, she quickly compared @aleyda and me π³.
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I feel like a newbie and an apprentice compared to Aleyda; nevertheless, the results grabbed my attention.
Using the new Add-on, we can compare search queries (and URLs) by looking at the entities behind top search results (or the entities behind single or multiple URLs).
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We begin with two queries (Aleyda Solis and Andrea Volpini) and let the Add-on analyze the respective SERPs to highlight similarities and differences.
We both share, for example the entities:
- SEO (duh)
- entrepreneur (this also makes sense)