  Prosopography is an investigation of the common characteristics of a group of
people in history, by a collective study of their lives. It involves a study of
biographies to solve historical problems. If such biographies are unavailable,
surviving documents and secondary biographical data are used. Quantitative
prosopography involves analysis of information from a wide variety of sources
about "ordinary people". In this paper, we present a machine learning framework
for automatically designing a people gazetteer which forms the basis of
quantitative prosopographical research. The gazetteer is learnt from the noisy
text of newspapers using a Named Entity Recognizer (NER). It is capable of
identifying influential people from it by making use of a custom designed
Influential Person Index (IPI). Our corpus comprises of 14020 articles from a
local newspaper, "The Sun", published from New York in 1896. Some influential
people identified by our algorithm include Captain Donald Hankey (an English
soldier), Dame Nellie Melba (an Australian operatic soprano), Hugh Allan (a
Canadian shipping magnate) and Sir Hugh John McDonald (the first Prime Minister
of Canada).
