I want to train my application for phrase similarity. I want my model to predict similarity score for phrases as shown in below examples. ex-

International Business Machines = I.B.M
Synergy Telecom = SynTel
Beam inc = Beam Incorporate
Sir J J Smith = Johnson Smith
Alex, Julia = J Alex
James B. D. Joshi = James Joshi
James Beaty, Jr. = Beaty

Is there any dataset available to train this type of model?

  • $\begingroup$ Can you please elaborate with an example of the prediction score you would want your model to predict? Is it going to be binary decision like similar(1) and not similar(0) OR you want percentage of similarity between given phrases? $\endgroup$
    – Preet
    Feb 10, 2019 at 11:37

2 Answers 2


This is a difficult problem, but definitely worth exploring.

An interesting resource to look into is DBpedia. It aims to extract structured information from the Wikipedia project. It is available under a free license (CC-BY-SA).

You can conveniently explore the project online, e.g.:

Note that you are restricted to the extensive but ending knowledge on Wikipedia, for example Synergy Telecom/SynTel seems not to have an entry. Your creativity would be required to overcome this limitation.


This seems to correspond to entity linking or possibly named entity coreference. You might find some datasets here.


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