Long-Distance Time-Event Relation Extraction
Alessandro Moschitti, Siddharth Patwardhan, Chris Welty · Iris (University of Trento) · 2013
This paper proposes state-of-the-art mod-els for time-event relation extraction (TERE). The models are specifically de-signed to work effectively with relations that span multiple sentences and para-graphs, i.e., inter-sentence TERE. Our main idea is: (i) to build a computational representation of the context of the two target relation arguments, and (ii) to en-code it as structural features in Support Vector Machines using tree kernels. Re-sults on two data sets – Machine Read-ing and TimeBank – with 3-fold cross-validation show that the combination of traditional feature vectors and the new structural features improves on the state of the art for inter-sentence TERE by about 20%, achieving a 30.2 F1 score on inter-sentence TERE alone, and 47.2 F1 for all TERE (inter and intra sentence combined). 1