Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia
Lexical complexity plays a central role in readability, particularly for dyslexic children and poor readers because of their slow and laborious decoding and word recognition skills. Although some features to aid readability may be common to most languages (e.g., the majority of {`}easy{'} words are of low frequency), we believe that lexical complexity is mainly language-specific. In this paper, we define lexical complexity for French and we present a pilot study on the effects of text simplification in dyslexic children. The participants were asked to read out loud original and manually simplified versions of a standardized French text corpus and to answer comprehension questions after reading each text. The analysis of the results shows that the simplifications performed were beneficial in terms of reading speed and they reduced the number of reading errors (mainly lexical ones) without a loss in comprehension. Although the number of participants in this study was rather small (N=10), the results are promising and contribute to the development of applications in computational linguistics.
Code (0)
등록된 구현이 없습니다.
Tasks
Reading ComprehensionText SimplificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French
Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluati…
Lexical SimplificationText SimplificationWord EmbeddingsCTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool
Linguistic complexity research being a very actively developing field, an increasing number of text analysis tools are created that use natural language processing techniques for the automatic extraction of quantifiable …
DiversityC3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings
We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with…
SentenceSentence EmbeddingsLexical Manifold Reconfiguration in Large Language Models: A Novel Architectural Approach for Contextual Modulation
Contextual adaptation in token embeddings plays a central role in determining how well language models maintain coherence and retain semantic relationships over extended text sequences. Static embeddings often impose con…
Language ModelingLanguage ModellingRepresentation LearningSentence+1How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?
While non-autoregressive (NAR) models are showing great promise for machine translation, their use is limited by their dependence on knowledge distillation from autoregressive models. To address this issue, we seek to un…
DiversityKnowledge DistillationMachine TranslationTranslation