I recently wrote about the results of trying out my M&A entity extraction project that is smart enough to create simple graphs of which company has done what with which other company.
For a side project very much in alpha it stood up pretty well against the best of the other offerings out there. At least in the first case I looked at. Here are two more complex examples chosen at random
Test 1 – M&A activity with multiple participants
Syracuse
It shows which organizations have been involved in the purchase, which organization sold the assets (Frontier) and the fact that the target entity is an organization called Ziply Fiber.
To improve, it could make it clearer that Ziply is a new entity being created rather than the purchase of an entity already called Ziply from Frontier. Also to identify that this is related to US North West assets. But otherwise pretty good.
Expert.ai
As before, it’s really good at identifying all the organizations in the text, even the ones that aren’t relevant to the story, e.g. Royal Canadian Mounted Police.
The relations piece is patchy. From the headline it determines that Searchlight Capital Partners is completing an acquisition of some operations, and also there is a relationship between the verb ‘complete’ and the assets of Frontier Communications. Pretty good result from this sentence, but not completely clear that there is an acquisition of assets.
Next sentence has a really good catch that Searchlight is forming Ziply
It only identifies one of the other parties involved in the transaction. It doesn’t tie the ‘it’ to Searchlight – you’d have to infer that from another relationship. And it doesn’t flag any of the other participants.
Test 2 – Digest Article
Article: Deals of the day-Mergers and acquisitions
Syracuse
It’s identifying 7 distinct stories. There are 8 bullet points in the Reuters story – one of which is about something that isn’t happening. Syracuse picks all of the real stories. It messes up Takeaway.com’s takeover of Just Eat by separating out Takeway and com as two different organizations, but apart from that looks pretty good.
I’m particularly gratified how it flags Exor as the spender and Agnelli as another kind of participant in the story about Exor raising its stake in GEDI. Agnelli is the family behind Exor, so they are involved, but strictly speaking the company doing the buying is Exor.
Expert.ai
Most of the entities are extracted correctly. A couple of notable errors:
- It finds a company called ‘Buyout’ (really this is the description of a type of firm, not the name of the firm)
- It also gets Takeaway.com wrong – but where Syracuse split this into two entities, Expert.ai flags it as a URL rather than a company (in yellow in the second image below)
The relationship piece is also pretty impressive from an academic point of view, but hard to piece together what is really going on from a practical point of view. Take the first story about Mediaset as an example and look at the relationships that Expert.ai identifies in the 4 graphs below. First one identifies that Mediaset belongs to Italy and is saying something. The other 3 talk about an “it” doing various things, but don’t tie this ‘it’ back to Mediaset.
Conclusion
Looking pretty good for Syracuse, if I say so myself :D.
Be good, work hard, get lucky 