AQG: Arabic Question Generator

The Arabic natural language process (ANLP) community does not have an automatic generator of questions for texts in the Arabic language. Our objective is to provide it one. This paper presents a novel automatic question generation approach that generates questions as a form of support for children learning through the platform QUIZZITO. Our approach combines the semantic role labelling of PropBank (SRL) and the flexibility of question models. It essentially relates to an approach of instantiation model of representation based on an analysis focused on the semantics. This allowed us to capture the maximum sense of sentence given the flexibility of the grammar of the Arabic language. This model was written in a set of Patterns and Templates based on the REGEX languages. Our goal is to enrich Quizzito's online quiz platform, which contains more than 254.5k quizzes, and to provide it with a generator of Arabic language questions for children's texts. Our Arabic Question Generator system (AQG) is functional and reaches up to 86% f-measure.

A System for Medical Information Extraction and Verification from Unstructured Text

A wealth of medical knowledge has been encoded in terminologies like SNOMED CT, NCI, FMA, and more. However, these resources are usually lacking information like relations between diseases, symptoms, and risk factors preventing their use in diagnostic or other decision making applications. In this paper we present a pipeline for extracting such information from unstructured text and enriching medical knowledge bases. Our approach uses Semantic Role Labelling and is unsupervised. We show how we dealt with several deficiencies of SRL-based extraction, like copula verbs, relations expressed through nouns, and assigning scores to extracted triples. The system have so far extracted about 120K relations and in-house doctors verified about 5k relationships. We compared the output of the system with a manually constructed network of diseases, symptoms and risk factors build by doctors in the course of a year. Our results show that our pipeline extracts good quality and precise relations and speeds up the knowledge acquisition process considerably.