Language Modeling by Clustering with Word Embeddings for Text Readability Assessment

We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that the features formed on our clustering-based language model significantly improve the previously known results for the same corpus in readability prediction. We also evaluate the task of sentence matching based on semantic relatedness using the Wiki-SimpleWiki corpus and find that our features lead to superior matching performance

Rasch-Derived latent trait measurement of outcomes: insightful use leads to precision case management and evidence-based practices in functional healthcare

The use of Rasch-derived latent trait measurement of outcomes for persons with chronic disease and disablement evolved from other fields, particularly education. Person-metrics is the measurement of how much chronic disease and disablement affects an individual's daily activities physically, cognitively, and through vocational and social role participation. The ability of the Rasch model to assume that the probability of a given person/ item interaction is governed by the difficulty of the item and the ability of the person is invaluable to disability measurement. The difference between raw scores and true measures is illustrated by an example of a patient whose physical difficulty is rated on rising from a wheelchair and walking 100m (known to be more difficult), and then walking an additional 200m. Though number ratings of 0-1-2 are assigned to these tasks, they are not equidistant, and only a true measure shows the actual levels of physical difficulty

Composition and Potential Health Benefits of Pomegranate: A Review

Background: Pomegranate (Punica granatum L.) fruits are widely consumed and used as preventive and therapeutic agents since ancient times. Pomegranate is a rich source of a variety of phytochemicals, which are responsible for its strong antioxidative and anti-inflammatory potential. Objective: The aim of this review is to provide an up-to-date overview of the current knowledge of chemical structure and potential health benefits of pomegranate. Methods: A comprehensive search of available literature. Results: The review of the literature confirms that juice and extracts obtained from different parts of this plant, including fruit peel, seeds, and leaves exert health benefits in both in vitro and in vivo studies. The antidiabetic, antihypertensive, antimicrobial and anti-tumour effects of pomegranate fruit are of particular scientific and clinical interest. Conclusion: Further investigations are required to clarify the mechanism of action of the bioactive ingredients and to reveal full potential of pomegranate as both preventive and therapeutic agent