The iRead4Skills intelligent complexity analyzer

dc.contributor.authorAissa, W.
dc.contributor.authorAmaro, R.
dc.contributor.authorAntunes, D.
dc.contributor.authorBañeras-Roux, T.
dc.contributor.authorBaptista, J.
dc.contributor.authorCatala, A.
dc.contributor.authorCorreia, L.
dc.contributor.authorFrançois, T.
dc.contributor.authorGarcia, M.
dc.contributor.authorIzquierdo-Álvarez, M.
dc.contributor.authorMamede, N.
dc.contributor.authorMartins, V.
dc.contributor.authorNeves. M.
dc.contributor.authorRibeiro, E.
dc.contributor.authorRodriguez Rey, S.
dc.contributor.authorVanzeveren, E.
dc.contributor.editorHabernal, Ivan
dc.contributor.editorSchulam,Peter
dc.contributor.editorTiedemann, Jörg
dc.date.accessioned2026-09-24T10:46:54Z
dc.date.issued2025
dc.date.updated2026-09-24T11:37:22Z
dc.description.abstractWe present the iRead4Skills Intelligent Complexity Analyzer, an open-access platform specifically designed to assist educators and content developers in addressing the needs of low-literacy adults by analyzing and diagnosing text complexity. This multilingual system integrates a range of Natural Language Processing (NLP) components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. It assigns four tailored difficulty levels using state-of-the-art models, and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. Each component of the system is supported by experiments comparing alternative models on manually annotated data.eng
dc.event.date2025
dc.event.locationSuzhou, Chinaeng
dc.event.titleConference on Empirical Methods in Natural Language Processing (EMNLP)
dc.event.typeConferênciapt
dc.identifier.citationAissa, W., Amaro, R., Antunes, D., Bañeras-Roux, T., Baptista, J., Catala, A., Correia, L., François, T., Garcia, M., Izquierdo-Álvarez, M., Mamede, N., Martins, V., Neves, M., Ribeiro, E., Rodriguez Rey, S., & Vanzeveren, E. (2025). The iRead4Skills intelligent complexity analyzer. In I. Habernal, P. Schulam, & J. Tiedemann (Eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System demonstrations (pp. 73-84). ACL. https://doi.org/10.18653/v1/2025.emnlp-demos.6
dc.identifier.doi10.18653/v1/2025.emnlp-demos.6
dc.identifier.isbn979-8-89176-334-0
dc.identifier.urihttps://hdl.handle.net/10071/38624
dc.language.isoeng
dc.pagination73 - 84
dc.peerreviewedyes
dc.publisherAssociation for Computational Linguistics (ACL)
dc.relationUID/03213
dc.relationUIDB/50021/2020
dc.relation.ispartofProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System demonstrations
dc.rightsopenAccess
dc.titleThe iRead4Skills intelligent complexity analyzereng
dc.typeconferenceObject
iscte.alternateIdentifiers.scopus2-s2.0-105039489331
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-113599

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