An Empirical Analysis of Linguistic, Typographic, and Structural Features in Simplified German Texts
Authors: Alessia Battisti, Sarah Ebling, Martin Volk (University of Zurich, 2019)
Topic: Text Simplification

Abstract
We investigate a newly compiled corpus of simplified German texts for evidence of multiple complexity levels using unsupervised machine learning techniques. We apply linguistic features used in previous supervised machine learning research and additionally exploit structural and typographic characteristics of simplified texts. The results show a difference in complexity among the texts investigated, with optimal partitioning solutions ranging between two and four clusters. They demonstrate that both linguistic and structural/typographic features are constitutive of the clusters.

Introduction
Simplified language aims to provide comprehensible information to people with reduced reading abilities. This group includes individuals with cognitive impairments, learning disabilities, prelingually deaf people, functionally illiterate individuals, and foreign language learners (Bredel and Maaß, 2016). Simplified language is characterized by reduced lexical and syntactic complexity and includes images, structured layouts, and explanations of difficult words. In German, several guidelines define which structures should be avoided, which need to be paraphrased, and which are understandable (Bundesministerium für Arbeit und Soziales, 2011; Inclusion Europe, 2009; Maaß, 2015; Netzwerk Leichte Sprache, 2013). Various countries have recognized simplified language as a means of inclusion, enabling the target populations to access information about their legal rights and participate in society. German-speaking countries have promoted simplified language more recently, particularly after the ratification of the United Nations Convention on the Rights of Persons with Disabilities (United Nations, 2006) in Austria (2008), Germany (2009), and Switzerland (2014). As a result, large amounts of simplified German texts have become available. More recently, simplified German has been conceptualized as a construct with multiple complexity levels (Bock, 2014; Bredel and Maaß, 2016; Kellermann, 2014). However, these proposals are still theoretical, as no sets of guidelines exist that define these levels with respect to linguistic or other features. The social franchise network Capito, a provider of simplification services and training courses for simplified language translators, recognizes three levels of simplified German corresponding to the Common European Framework of Reference for Language (CEFR) levels A1, A2, and B1. However, being commercially oriented, Capito does not make its CEFR adaptation publicly available. In this paper, we present an unsupervised machine learning (clustering) approach to analyze texts in simplified German with the goal of investigating evidence of multiple complexity levels. To the best of our knowledge, this is the first study of its kind. We apply linguistic features used in previous supervised machine learning research (classification) and also exploit structural and typographic characteristics of simplified texts, which have been described in the literature but not incorporated into clustering or classification approaches in the context of simplified language. The remainder of this paper is structured as follows: Section 2 presents the research background. Section 3 describes our approach, introducing a novel dataset (Section 3.1), the feature design and engineering (Section 3.2), the clustering experiments (Section 3.3), and a discussion thereof (Section 3.4). Section 4 offers a conclusion and an outlook on future research questions.

Conclusion
In this paper, we have presented the first approach to investigating simplified German texts by means of unsupervised machine learning techniques as a basis for future readability assessment studies on this language variety. In addition, we have introduced novel features that have been described in the literature but not incorporated into machine learning (clustering and/or classification) approaches in the context of simplified language, notably: number of images, number of paragraphs, number of lines, number of words of a specific font type, and adherence to a one-sentence-per-line rule. Our findings provide evidence that existing texts are not simplified at a unique complexity level of German. We have demonstrated that features based on structural information are capable of accounting for the different complexity levels found. As a next step, we will use the results of the experiments presented in this paper to establish a framework of inductively generated complexity levels. This framework will serve as the basis for readability assessment in the context of simplified German. Knowledge derived from our study can also inform automatic and manual approaches to the simplification of German.