Temporal Relation Classification in Persian and English contexts

This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses support vector machines (SVMs) equipped by combinations of simple, convolution tree, and string subsequence kernels (SSK). In order to evaluate the algorithm, we have developed a Persian TimeBank (PTB) corpus. PTRC not only increases the performance of the classification by applying new features and SSK, but also alleviates the probable adverse effects of the Free Word Orderness (FWO) of Persian on temporal relation classification. We have also applied our proposed algorithm to two standard corpora on English (i.e., TimeBank and TempEval-2) to measure the efficiency of the new features and SSK. The experiments show the accuracies of 65.6%, 59.53%, 50.2%, and 62.17% on an augmented version of PTB, TimeBank, tasks E and F of TempEval-2, respectively. Consequently, we have achieved the third best result on TimeBank, and the second best result on the task F of TempEval-2.