Text Complexity

Необходимость оценить сложность текста для читателя может возникнуть в разных ситуациях: составление текстов договоров и законов, создание инструкций к приборам, написание учебников родного или иностранного языка, подбор литературы для внеклассного чтения. Особенно интересна оценка сложности учебных текстов для детей, поскольку к таким текстам предъявляется сразу несколько требований, отчасти противоречащих друг другу. Дети должны хорошо понимать эти тексты, они должны быть актуальны и интересны и одновременно учить читателей как новым концепциям, так и новым словам и конструкциям. Сейчас возрастная маркировка текстов для детей проводится экспертами вручную, что делает процесс долгим и трудоемким, а результат субъективным. В статье предлагается метод автоматической классификации текстов по сложности с использованием нейросетевой модели. Этот метод предполагается использовать для создания корпуса текстов детской литературы с разметкой по возрасту (в рамках НКРЯ). Качество предсказаний нашей модели достигло 0,92, она достаточно хорошо учитывает разнообразие лексики и набор тем. Появление автоматического механизма, с приемлемой точностью оценивающего степень сложности текста, позволит в короткие сроки создать представительный корпус текстов, написанных для детей, с возможностью подбора в нем текстов, заведомо понятных детям заданного возраста. Такой корпус будет востребован и учителями, и родителями, и переводчиками художественной литературы, и лингвистами, и всеми, кому важна возможность подбора понятных детям художественных текстов.

Prompted by the advent of new standards for increased text complexity in elementary classrooms in the United States, the current integrative review investigates relationships between the level of text difficulty and elementary students’ reading fluency and reading comprehension.  After application of content and methodological criteria, a total of 26 research studies were reviewed.  Characteristics of the reviewed studies are reported including the different conceptualizations of text, reader, and task interactions. Regarding the relationships between text difficulty and reading fluency and comprehension, for students’ reading fluency, on average, increased text difficulty level was related to decreased reading fluency, with a small number of exceptions.  For comprehension, on average, text difficulty level was negatively related to reading comprehension, although a few studies found no relationship.  Text difficulty was widely conceptualized across studies and included characteristics particular to texts as well as relationships between readers and texts. Implications for theory, policy, curriculum, and instruction are discussed.

The purpose of the study was to examine the relationship between the challenge level of text and early readers' reading comprehension.  This relationship was also examined with consideration to students' word recognition accuracy and reading rate.  Participants included 636 students, in grades one through three, in a southeastern state.  Results suggest that students reading texts well above their actual grade levels, even with sufficient accuracy scores, scored significantly lower on comprehension than students reading texts at their actual grade level.  This result also held regardless of students' reading rates.  Findings signal the importance of considering text level during instruction and suggest some caution is warranted when pushing students into texts well above their grade levels.

Dalvean, Michael and Galbadrakh Enkhbayar. 2018. Standard readability measures are based on the readability of non-fiction texts. Linguistic Research 35(Special Edition), 137-170. This means that the validity of the measures when applied to fiction texts is questionable. Thus, the scores given to fiction texts using such indices may be invalid when used by English teachers to identify fiction texts of appropriate difficulty for students with various reading ability levels. This paper attempts to address this problem by 1) developing a readability measure specifically designed for fiction texts and 2) applying it to 200 English fiction texts. A corpus, consisting of 100 adults' and 100 children's texts, is used for the analysis. In the initial modeling, several standard readability measures are used as variables, and machine learning is used to create a classifier which is able to classify the corpus with an accuracy of 84%. A second classifier is then created using linguistic variables rather than standard readability measures. The latter classifier is able to classify the corpus with an accuracy of 89%, indicating that the standard readability measures are less accurate in classifying fiction texts than linguistic variables. Due to its higher accuracy, the latter classifier is then used to provide a linear complexity or 'readability' rank for each text. The ranking using the linguistic-based classifier provides an more accurate method of determining which texts to choose for students according to their reading levels than the standard readability measures. Importantly, the ranking instantiates a fine-grained increase in complexity. This means that the ranking can be used by an English teacher to select a sequence of texts that represent an increasing challenge to a student without there being a frustratingly discrete rise in difficulty. (Canberra College ･ Southern Taiwan University of Science and Technology)

This study addresses the distribution of words in texts at different points of schooling. The first aim was to identify a core vocabulary that accounts for the majority of the words in texts through the lens of morphological families. Results showed that 2,451 morphological families, averaging 4.61 members, make up the core vocabulary of school texts. The 11,298 words in the 2,451 morphological families account for 58% of the approximately 19,500 most frequent words in written English. The majority of the morphological families appear by the end of the elementary school period (85%), but a small group of morphological families (15%) is added through the middle to high school period. Analyses of the ranks of words across grade bands indicated that late-appearing words gain in prominence in higher level texts as some elementary-level words become less frequent. The second aim of the study was to determine the degree to which the core vocabulary accounted for the words in an independent but critical set of texts: the exemplar texts identified within the Common Core State Standards. The 2,451 families accounted for 97.1% (grades K and 1) to 89.1% (grade 11 through college) of the total words in texts and 95.6% (grades K and 1) to 74.9% (grade 11 through college) of the unique words in texts. Implications of the findings on the nature and role of the core vocabulary in complex texts are suggested for researchers, curriculum developers, and publishers.

This article argues that the current emphasis on college and career readiness by high school graduation fails to recognize the dynamic needs of adults in reading development across the lifespan. A model for college reading programs is designed, drawing on evidence that reading development continues beyond college and career readiness levels. It is proposed that the content of such programs should emphasize elements of reading such as metacognition, critical analysis, multimodal reading, creative thinking, and flexibility with electronic forms of reading. Opportunities to develop such capacities would serve both college students and other adult populations in acquiring the literacies required for professional, social, and personal well-being in the 21st century.

Background The vocabulary found in a given text often contributes to the complexity of that text. The vocabularies of stories and of informational texts can be quite different—differences that can influence students' ability to comprehend and remember texts. One misconception is that the vocabulary of informational texts is considerably harder than the vocabulary of narrative texts. But this pattern is not always so—especially in elementary texts where content words (eg, photosynthesis) are

We present a computational notion of Lexical
Tightness that measures global cohesion of content
words in a text. Lexical tightness represents
the degree to which a text tends to use words
that are highly inter-associated in the language.
We demonstrate the utility of this measure for
estimating text complexity as measured by US
school grade level designations of texts. Lexical
tightness strongly correlates with grade level in
a collection of expertly rated reading materials.
Lexical tightness captures aspects of prose
complexity that are not covered by classic readability
indexes, especially for literary texts. We
also present initial findings on the utility of this
measure for automated estimation of complexity
for poetry.

Studies on simple language and simplification are often based on datasets of texts, either for children or learners of a second language. In both cases, these texts represent an example of simple language, but simplification likely involves different strategies. As such, this data may not be entirely homogeneous in terms of text simplicity. This study investigates linguistic properties and specific simplification strategies used in Russian texts for primary school children with different language backgrounds and levels of language proficiency. To explore the structure and variability of simple texts for young readers of different age groups, we have trained models for multiclass and binary classification. The models were based on quantitative features of texts. Subsequently, we evaluated the simplification strategies applied to readers of the same age with different linguistic backgrounds. This study is particularly relevant for the Russian language material, where the concept of easy and plain language has not been sufficiently investigated. The study revealed that the three types of texts cannot easily be distinguished from each other by judging the performance of multiclass models based on various quantitative features. Therefore, it can be said that texts of all types exhibit a similar level of accessibility to young readers. In contrast, binary classification tasks demonstrated better results, especially in the R-native vs. non R-native track (with 0.78 F1-score), these results may indicate that the strategies used for adapting or creating texts for each type of audience are different.

The Common Core set a standard for all children to read increasingly complex texts throughout schooling. The purpose of the present study was to explore text characteristics specifically in relation to early-grades text complexity. Three-hundred-fifty primary-grades texts were selected and digitized. Twenty-two text characteristics were identified at four linguistic levels, and multiple computerized operationalizations were created for each of the 22 text characteristics. A researcher-devised text-complexity outcome measure was based on: teacher judgment of text complexity in the 350 texts; and text complexity as gauged from student responses using a maze task for a subset of the 350 texts. Analyses were conducted using a logical analytical progression typically used in machine-learning research. Random forest regression was the primary statistical modeling technique. Nine text characteristics were most important for early-grades text complexity including word structure (decoding demand and number of syllables in words), word meaning (age of acquisition, abstractness, and word rareness), and sentence and discourse-level characteristics (intersentential complexity, phrase diversity, text density/information load, and non-compressibility). Notably, interplay among text characteristics was important to explanation of text complexity, particularly for subsets of texts.