HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Age Recommendation for Texts

Abstract : The understanding of a text by a reader or listener is conditioned by the adequacy of the text's characteristics with the person's capacities and knowledge. This adequacy is critical in the case of a child since her/his cognitive and linguistic skills are still under development. Hence, in this paper, we present and study an original natural language processing (NLP) task which consists in predicting the age from which a text can be understood by someone. To do so, this paper first exhibits features derived from the psycholinguistic domain, as well as some coming from related NLP tasks. Then, we propose a set of neural network models and compare them on a dataset of French texts dedicated to young or adult audiences. To circumvent the lack of data, we study the idea to predict ages at the sentence level. The experiments first show that the sentence-based age recommendations can be efficiently merged to predict text-based recommendations. Then, we also demonstrate that the age predictions returned by our best model are better than those provided by psycholinguists. Finally, the paper investigates the impact of the various features used in these results.
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download

Contributor : Gwénolé Lecorvé Connect in order to contact the contributor
Submitted on : Monday, June 15, 2020 - 12:23:25 PM
Last modification on : Tuesday, May 10, 2022 - 4:00:15 PM


Files produced by the author(s)


  • HAL Id : hal-02868118, version 1


Alexis Blandin, Gwénolé Lecorvé, Delphine Battistelli, Aline Étienne. Age Recommendation for Texts. Language Resources and Evaluation Conference (LREC), May 2020, Marseille, France. ⟨hal-02868118⟩



Record views


Files downloads