ArabRelat: Arabic Relation Extraction using Distant Supervision.

Relation Extraction is an important preprocessing task for a number of text mining applications, including: Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a novel Arabic relation extraction method that leverages linguistic features of the Arabic language in Web data to infer relations between entities. Due to the lack of labeled Arabic corpora, we adopt the idea of distant supervision, where DBpedia, a large database of semantic relations extracted from Wikipedia, is used along with a large unlabeled text corpus to build the training data. We extract the sentences from the unlabeled text corpus, and tag them using the corresponding DBpedia relations. Finally, we build a relation classifier using this data which predicts the relation type of new instances. Our experimental results show that the system reaches 70% for the F-measure in detecting relations.