Machine Reading of Historical Events

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.