Using Bidirectional LSTM and Shortest Dependency Path for Classifying Arabic Temporal Relations

Abstract The classification of temporal relations between two entities is a crucial task for the comprehension of natural languages. Several methods have been proposed, but most of them have focused on English and several other languages. Contrarily, Arabic has received less attention for the classification of temporal relations. Recently, methods based on deep learning have obtained more relevant results than traditional methods. These latter have used several features, but they have lacked the modeling of syntactic structures. In this context, we present a first model for classifying temporal relations between pairs of events (E-E) or an event and a temporal expression (E-T) in Arabic intra-sentence. Our idea is to combine two branches of bidirectional LSTM networks to extract syntactic information by modeling the Shortest Dependency Path (SDP) between two target entities as well as the whole sentence sequence. Each word in the sentence is concatenated with its Parts Of Speech (POS). For the SDP branch, each word is concatenated with its POS as well as its generated dependency relations. To evaluate our model, and due to the lack of publicly available Arabic resources, we create an annotated Arabic corpus for temporal relations between E-E or E-T based on TimeML specifications. In addition, we propose a data augmentation method using transitivity and symmetry rules for temporality. The experimental results based on our annotated corpus give 84.6% of F1 for the classification of temporal relations between E-E, and 73.32% of F1 for the same task between E-T.