KUL: A data-driven approach to temporal parsing of documents
Oleksandr Kolomiyets, KU Leuven, Marie‐Francine Moens · Lirias · 2013
This paper describes a system for temporal processing of text, which participated in the Temporal Evaluations 2013 campaign. The system employs a number of machine learning classifiers to perform the core tasks of: identification of time expressions and events, recognition of their attributes, and estimation of temporal links between recognized events and times. The central feature of the proposed system is temporal parsing – an approach which identifies temporal relation arguments (event-event and event-timex pairs) and the semantic label of the relation as a single decision.