Next step was to see if I can do something useful with this. In past lives customers have told me about the importance of tracking certain signals or events in a company’s lifecycle, e.g. making an acquisition, expanding to a new territory, making a new senior hire etc.
So I gave it a go, initially looking purely at whether I could train an algorithm to pick out key staffing changes. Results below are 20 random topics pulled from my from my first attempt showing the good, bad and ugly. The numbers are the confidence scores that the algorithm chose for each entity in the topic.
I’ll give myself a B for a decent first prototype.
I do wonder who else out there is working on this sort of thing. From what I can see in the market ML is used to classify articles (e.g. “this article is about a new hire”) but I couldn’t see any commercial offering that goes to the level of “which org hired who into what role”.
If I were to take this further I would be training specialist models on each different type of topic. I wonder if there is something like a T5-style model to rule them all that can handle all this kind of intelligent detailed topic understanding?
Title
OSE Immunotherapeutics Announces the Appointment of Dominique Costantini as Interim CEO Following the Departure of Alexis Peyroles
Pulled out the two key items but: didn’t do a great job of the Entity (Britain’s Barclays Plc was treated as one entity) and doesn’t understand the pluralised role name. Model was not trained to look for where the role is based, so haven’t identified that these roles are specifically in Australia
Title
Trulioo Appoints Michael Ramsbacker as Chief Product Officer
Similar to the Elastrin story it pulls out the title and the division but treats them as different roles; also only assigns one of the found roles to Mr Krouner. Also is a bit ‘greedy’ at identifying the Org – the part in parentheses is redundant
Title
Stertil-Koni Names Supply Chain Sales Pro Scott Steinhardt as Vice President of Sales
Be good, work hard, get lucky 