To my surprise, this tool finds no problems with the article and gives it a B.
(I'm really tickled by the tool, like the idea, but based on a totally not rigorous sample, seems like the tool leans a bit too much into sort of sentence structure analysis and elides some semantics?)
Thanks for linking this issue, and I'll certainly look into it. I agree with your analysis on Twitter: manually annotating the article should yield the following issues:
1. Focus: she was struck by a Toyota Corolla (emphasis is placed on the VRU)
2. Object: she was struck by a Toyota Corolla (agent is referred to as an object, rather than a person, e.g. driver of the Corolla)
3. Object: a 2010 Toyota Corolla being driven by a 29-year-old Brookline man, was driving east (again, the wording personifies the vehicle instead of assigning agency to the driver heading eastward)
I will need to debug this particular example further, but it appears the "2010 Toyota Corolla" and "Toyota Corolla" are not being classified as CARLIKE, a label I trained an NER model on to help deal with all the ways you can name a vehicle: year-make-model, make-model, year-model, model, model-ish, generic terms like truck/pickup/pick-up etc., short-hands like Chevy instead of Chevrolet, etc.
Furthermore, the tool is not identifying "woman" as a VRU. It's a little bit ambiguous because while "[she] entered the roadway", it's not 100% clear that means she is a VRU: a driver/vehicle can enter a roadway too.
Thread: https://twitter.com/evanjfields/status/1387131251811831812/
Article: https://www.bostonglobe.com/2021/04/27/metro/woman-28-seriou...
To my surprise, this tool finds no problems with the article and gives it a B.
(I'm really tickled by the tool, like the idea, but based on a totally not rigorous sample, seems like the tool leans a bit too much into sort of sentence structure analysis and elides some semantics?)