All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Enhancing the Communication of Law: a cross-disciplinary investigation applying information technology

Law is pervasive in culture. It is a form of communication between government and citizens. When effective, it is a tool of government policy. If poorly designed,law results in unnecessary costs to society. Impediments to understanding of the law limits and distorts democratic participation. Yet, historically, the law has been inaccessible to most. Thus enhancing the communication of law is an important and standing problem. Much work has been done (for example through the plain language movement) to improve the communication of law. Nonetheless, the law remains largely unreadable to non-legal users. This thesis applies information technology to investigate and enhance the communication of law. To this end, this thesis focusses on four main areas.To improve the readability of law, it must be better described as a form of language. Corpus linguistics is applied for this purpose. A linguistic description of contract language arose from this work, which, along with the corpus itself, has been made available to the research community. The thesis also describes work for the automatic classification of text in legal contracts by legal function.Reliable measures for the readability of law are needed, but they do not exist. To develop such measures, gold standard data is needed to evaluate possible measures.To create this gold standard data, the research engaged citizen scientists, in the form of the online “crowd”. However, methods for creating and using such user assessments for readability are rudimentary. The research therefore investigated,developed and applied a number of methods for collecting user ratings of readability in an online environment. Also, the research applied machine learning to investigate and identify linguistic factors that are specifically associated with language difficulty of legislative sentences. This resulted in recommendations for improving legislative readability. A parallel line of investigation concerned the application of visualization to enhance the communication of law. Visualization engages human visual perception and its parallel processing capacities for the communication of law. The research applied computational tools: natural language processing, graph characteristics and data driven algorithms. It resulted in prototype tools for automatically visualizing definition networks and automating the visualization of selected contract clauses. Also, the work has fostered an investigation of the nature of law itself. A “law as” framework is used to query the nature of law and illuminate law in new ways. The framework is re-assessed as a tool for the experimental investigation of law. This results in an enhanced description of law, applying a number of investigatory frames:law; communication; document; information; computation; design and complex systems theory. It also provides a contrastive study with traditional theories of law - demonstrating how traditional theories can be extended in the light of these multidisciplinary results. In sum, this thesis reports a body of work advancing the existing knowledge base and state of the art in respect of application of computational techniques to enhancing the communication of law