Fiche du document
2017
- handle: 10670/1.8pz0ar
- hal: hal-01584870
- doi: 10.1007/978-3-319-61425-0_5
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-61425-0_5
Ce document est lié à :
info:eu-repo/grantAgreement//644187/EU/Realising an Applied Gaming Eco-system/RAGE
info:eu-repo/semantics/OpenAccess
Mots-clés
Multi-Lingual Text Analysis Textual Complexity Comprehension Prediction Natural Language Processing Textual Cohesion Writing StyleSujets proches
Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Mihai A Dascalu et al., « ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity », HAL-SHS : sciences de l'éducation, ID : 10.1007/978-3-319-61425-0_5
Métriques
Partage / Export
Résumé
Assessing textual complexity is a difficult, but important endeavor, especially for adapting learning materials to students' and readers' levels of understanding. With the continuous growth of information technologies spanning through various research fields, automated assessment tools have become reliable solutions to automatically assessing textual complexity. ReaderBench is a text processing framework relying on advanced Natural Language Processing techniques that encompass a wide range of text analysis modules available in a variety of languages, including English, French, Romanian, and Dutch. To our knowledge, ReaderBench is the only open-source multilingual textual analysis solution that provides unified access to more than 200 textual complexity indices including: surface, syntactic, morphological, semantic, and discourse specific factors, alongside cohesion metrics derived from specific lexicalized ontologies and semantic models.