RelANE: Discovering Relations between Arabic Named Entities

In this paper, we describe the first tool that detects the semantic relation between Arabic named entities, henceforth RelANE. We use various supervised learning techniques to predict the word or the sequence of terms that can highlight one or more semantic relationship between two Arabic named entities. For each word in the sentence, we use its morphological, contextual and semantic features of entity types. We do not integrate a relation classes predefined in order to cover more relations that can be presented in sentences. Given that free Arabic corpora for this task are not available, we built our own corpus annotated with the required information. Plenty of experiments are conducted, and the preliminary results proved the effectiveness of our process that allows to extract semantic relation between Arabic NEs. We obtained promising results in terms of F-score when applied to our corpus.