Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts

Computational approaches to readability assessment are generally built and evaluated using gold standard corpora labeled by publishers or teachers rather than being grounded in observations about human performance. Considering that both the reading process and the outcome can be observed, there is an empirical wealth that could be used to ground computational analysis of text readability. This will also support explicit readability models connecting text complexity and the reader’s language proficiency to the reading process and outcomes. This paper takes a step in this direction by reporting on an experiment to study how the relation between text complexity and reader’s language proficiency affects the reading process and performance outcomes of readers after reading We modeled the reading process using three eye tracking variables: fixation count, average fixation count, and second pass reading duration. Our models for these variables explained 78.9%, 74% and 67.4% variance, respectively. Performance outcome was modeled through recall and comprehension questions, and these models explained 58.9% and 27.6% of the variance, respectively. While the online models give us a better understanding of the cognitive correlates of reading with text complexity and language proficiency, modeling of the offline measures can be particularly relevant for incorporating user aspects into readability models

Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review

In times of social media, crisis managers can interact with the citizens in a variety of ways. Since machine learning has already been used to classify messages from the population, the question is, whether such technologies can play a role in the creation of messages from crisis managers to the population. This paper focuses on an explorative research revolving around selected machine learning solutions for crisis communication. We present systematic literature reviews of readability assessment and text simplification. Our research suggests that readability assessment has the potential for an effective use in crisis communication, but there is a lack of sufficient training data. This also applies to text simplification, where an exact assessment is only partly possible due to unreliable or non-existent training data and validation measures

A Graph-based Readability Assessment Method using Word Coupling

This paper proposes a graph-based read-ability assessment method using word coupling. Compared to the state-of-the-art methods such as the readability for-mulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text fea-tures. Unlike the general bag-of-words model which assumes words are indepen-dent, our model correlates the words based on their similarities on readability. By applying TF-IDF (Term Frequency and Inverse Document Frequency), the cou-pled TF-IDF matrix is built, and used in the graph-based classification framework, which involves graph building, merging and label propagation. Experiments are conducted on both English and Chinese datasets. The results demonstrate both ef-fectiveness and potential of the method.