Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

Watanabe, Willian Massami; Cândido, Arnaldo; Amancio, Marcelo Adriano; Oliveira, Matheus de; Pardo, Thiago Alexandre Salgueiro; Fortes, Renata Pontin de Mattos; Aluísio, Sandra Maria

doi:10.1080/13614568.2010.542620

Cited by 16 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Educational FACILITA was a web application prototype which offered lexical elaborations and named entity labelling. A pilot study with low-literacy users reported improved text comprehension, though participants reported to be sometimes confused with the offered list of synonyms that had multiple meanings (Watanabe et al, 2010).…”

Section: Ats Projects For Social Goodmentioning

confidence: 99%

Automatic Text Simplification for Social Good: Progress and Challenges

Štajner

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

View full text Add to dashboard Cite

Since the late 1990s, automatic text simplification (ATS) was promoted as a natural language processing (NLP) task with great potential to make texts more accessible to people with various reading or cognitive disabilities, and enable their better social inclusion. Large multidisciplinary projects showed promising steps in that direction. Since 2010, the field started attracting more attention but at the cost of major shifts in system architecture, target audience, and evaluation strategies. Somewhere along the way, the focus has shifted from ATS for social good towards building complex endto-end neural architectures that are not aimed at any particular target population. This study presents the trajectory of ATS for social good, the main issues in current ATS trends, and the ways forward that could bring the field back to its initial goals.

show abstract

Section: Ats Projects For Social Goodmentioning

confidence: 99%

Automatic Text Simplification for Social Good: Progress and Challenges

Štajner

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

View full text Add to dashboard Cite

show abstract

“…However, when identifying complex words for individuals outside of their intended target population or domain, lexicon-based approaches perform less well [120]. For example, FACILITA [174] is designed to distinguish between Portuguese complex and non-complex words for Brazilian children using three dictionaries: (1) consisted of frequent words extracted from Brazilian newspapers, (2) contained concrete words, and (3) housed simple words that were believed to be "common to youngsters" [10]. FACILITA is very effective in helping young low literacy readers in Brazil.…”

Section: 23mentioning

confidence: 99%

Lexical Complexity Prediction: An Overview

2023

View full text Add to dashboard Cite

The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this paper, we present an overview of computational approaches to lexical complexity prediction focusing on the work carried out on English data. We survey relevant approaches to this problem which include traditional machine learning classifiers (e.g. SVMs, logistic regression) and deep neural networks as well as a variety of features, such as those inspired by literature in psycholinguistics as well as word frequency, word length, and many others. Furthermore, we introduce readers to past competitions and available datasets created on this topic. Finally, we include brief sections on applications of lexical complexity prediction, such as readability and text simplification, together with related studies on languages other than English.

show abstract

“…Lexical simplification (LS) is the process of substituting complex words or phrases with their simpler variants. It is an important factor in making texts more accessible for people with aphasia (Carroll et al, 1998;Devlin and Unthank, 2006;, dyslexia (Rello et al, 2013b,a), autism spectrum disorders (Orȃsan et al, 2018), cognitive impairments (Feng et al, 2009;Saggion et al, 2015), low literacy levels (Aluísio et al, 2008;Watanabe et al, 2010), deaf and hard-ofhearing people (Inui et al, 2003;Alonzo et al, 2020), children (De Belder et al, 2010), and nonnative speakers (Hirsh and Nation, 1992;Heilman et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

LeSS: A Computationally-Light Lexical Simplifier for Spanish

Štajner,

Ibáñez,

Saggion

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

View full text Add to dashboard Cite

Due to having knowledge of only basic vocabulary, many people cannot understand up-to-date written information and thus make informed decisions and fully participate in the society. We propose LeSS, a modular lexical simplification architecture that outperforms state-ofthe-art lexical simplification systems for Spanish. In addition to its state-of-the-art performance, LeSS is computationally light, using much less disk space, CPU and GPU, and having faster loading and execution time than the transformer-based lexical simplification models which are predominant in the field.

show abstract

Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

Cited by 16 publications

References 20 publications

Automatic Text Simplification for Social Good: Progress and Challenges

Automatic Text Simplification for Social Good: Progress and Challenges

Lexical Complexity Prediction: An Overview

LeSS: A Computationally-Light Lexical Simplifier for Spanish

Contact Info

Product

Resources

About