Do you plan to use the surrounding context? If yes, maybe you could try to split NER in two categories: PersonM and PersonF. Just an idea, never read or tried anything like it. You would need a training corpus with these classes.
You could add both the plain dictionary and the regex as NER features as well and check how it improves. 2016-06-28 18:56 GMT-03:00 Damiano Porta <damianopo...@gmail.com>: > Hello everybody, > > we built a NER model to find persons (name) inside our documents. > We are looking for the best approach to understand if the name is > male/female. > > Possible solutions: > - Plain dictionary? > - Regex to check the initial and/letters of the name? > - Classifier? (naive bayes? Maxent?) > > Thanks >
