ITL - International Journal of Applied Linguistics - Volume 165, Issue 2, 2014

Assessing text readability is a time-honored problem that has even more relevance in today’s information-rich world. This article provides background on how readability of texts is assessed automatically, reviews the current state-of-the-art algorithms in automatic modeling and predicting the reading difficulty of texts, and proposes new challenges and opportunities for future exploration not well-covered by current computational research.

In this paper, we analyse the differences between L1 acquisition and L2 learning and identify four main aspects: input quality and quantity, mapping processes, cross-lingual influence, and reading experience. As a consequence of these differences, we conclude that L1 readability measures cannot be directly mapped to L2 readability. We propose to calculate L2 readability for various dimensions and for smaller units. It is particularly important to account for the cross-lingual influence from the learner’s L1 and other previously acquired languages and for the learner’s higher experience in reading.In our analysis, we focus on lexical readability as it has been found to be the most influential dimension for L2 reading comprehension. We discuss the features frequency, lexical variation, concreteness, polysemy, and context specificity and analyse their impact on L2 readability. As a new feature specific to L2 readability, we propose the cognateness of words with words in languages the learner already knows. A pilot study confirms our assumption that learners can deduce the meaning of new words by their cognateness to other languages.

In this paper, we tackle three underresearched issues of the automatic readability assessment literature, namely the evaluation of text readability in less resourced languages, with respect to sentences (as opposed to documents) as well as across textual genres. Different solutions to these issues have been tested by using and refining READ‑IT, the first advanced readability assessment tool for Italian, which combines traditional raw text features with lexical, morpho-syntactic and syntactic information. In READ‑IT readability assessment is carried out with respect to both documents and sentences, with the latter constituting an important novelty of the proposed approach: READ‑IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario. By comparing the results of two versions of READ‑IT, adopting a classification‑ versus ranking-based approach, we also show that readability assessment is strongly influenced by textual genre; for this reason a genre-oriented notion of readability is needed. With classification-based approaches, reliable results can only be achieved with genre-specific models: Since this is far from being a workable solution, especially for less resourced languages, a new ranking method for readability assessment is proposed, based on the notion of distance.

Readability assessment can play a role in the evaluation of a simplification algorithm as well as in the identification of what to simplify. While some previous research used traditional readability formulas to evaluate text simplification, there is little research into the utility of readability assessment for identifying and analyzing sentence level targets for text simplification. We explore this aspect in our paper by first constructing a readability model that is generalizable across corpora and across genres and later adapting this model to make sentence-level readability judgments.First, we report on experiments establishing that the readability model integrating a broad range of linguistic features works well at a document level, performing on par with the best systems on a standard test corpus. Next, the model is confirmed to be transferable to different text genres. Moving from documents to sentences, we investigate the model’s ability to correctly identify the difference in reading level between a sentence and its human simplified version. We conclude that readability models can be useful for identifying simplification targets for human writers and for evaluating machine generated simplifications.

In this paper we present an application of associative lexical cohesion to the analysis of text complexity as determined by expert-assigned US school grade levels. Lexical cohesion in a text is represented as a distribution of pairwise positive normalized mutual information values. Our quantitative measure of lexical cohesion is Lexical Tightness (LT), computed as average of such values per text. It represents the degree to which a text tends to use words that are highly inter-associated in the language. LT is inversely correlated with grade levels and adds significantly to the amount of explained variance when estimating grade level with a readability formula. In general, simpler texts are more lexically cohesive and complex texts are less cohesive. We further demonstrate that lexical tightness is a very robust measure. We compute lexical tightness for a whole text and also across segmental units of a text. While texts are more cohesive at the sentence level than at the paragraph or whole-text levels, the same systematic variation of lexical tightness with grade level is observed for all levels of segmentation. Measuring text cohesion at various levels uncovers a specific genre effect: informational texts are significantly more cohesive than literary texts, across all grade levels.

Text simplification, defined narrowly, is the process of reducing the linguistic complexity of a text, while still retaining the original information and meaning. More broadly, text simplification encompasses other operations; for example, conceptual simplification to simplify content as well as form, elaborative modification, where redundancy and explicitness are used to emphasise key points, and text summarisation to omit peripheral or inappropriate information. There is substantial evidence that manual text simplification is an effective intervention for many readers, but automatic simplification has only recently become an established research field. There have been several recent papers on the topic, however, which bring to the table a multitude of methodologies, each with their strengths and weaknesses. The goal of this paper is to summarise the large interdisciplinary body of work on text simplification and highlight the most promising research directions to move the field forward.

Are rounded numbers easier to understand than exact numbers? Information in newspapers often takes the form of numerical expressions which pose comprehension problems for many people, including people with disabilities, low literacy levels or lack of access to advanced technology. The purpose of this paper is to motivate and describe a rule-based lexical component that simplifies numerical expressions in Spanish texts. We propose a simplification approach that makes news articles more accessible to readers with specials needs by rewriting difficult numerical expressions in a simpler way. We carried out a study that identifies powerful simplification strategies to simplify numerical information in a text by analysing a parallel corpus of original texts and their manual simplifications. The study is complemented with an analysis of simplifications obtained in response to a questionnaire where subjects were asked to produce simplifications of numerical expressions in context. Finally, we implemented and evaluated a simplification system that mimics the simplification strategies that were found to be effective.