Children have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a novel method which predicts the minimal age from which a text can be understood. This method analyses each sentence of a text using a recurrent neural network, and then aggregates this information to provide the text-level prediction. Different approaches are proposed and compared to baseline models, at sentence and text levels. Experiments are carried out on a corpus of 1, 500 texts and 160K sentences. Our best model, based on LSTMs, outperforms state-of-the-art results and achieves mean absolute errors of 1.86 and 2.28, at sentence and text levels, respectively.

Le système présenté permet la construction automatisée d’une base de connaissances sur des personnes et des organisations à partir d’une collection de documents. Il s’appuie sur de l’apprentissage distant pour l’extraction d’hypothèses de relations entre mentions d’entités qu’il consolide avec des informations orientées graphe.