Machine Reading of Historical Events
Or Honovich, Lucas Torroba Hennigen, Omri Abend, Shay B. Cohen · 2020
Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it.Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of taskspecific and general-purpose feature embeddings for the classification.Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms. 1 1 Code and data are available at https://github.com/ltorroba/ machine-reading-historical-events.* Equal contribution.Year Event text OTD 2005 107 die in Amagasaki rail crash in Japan.1939 BMI (Broadcast Music Incorporated) formed.1864 General Sherman's armies reach Savannah & 12 day siege begins.WOTD 1887 Buffalo Bill Cody's Wild West Show opens in London.1399 Henry IV is proclaimed King of England.1943 First Flight of the Gloster Meteor, Britain's first combat jet aircraft.