56,208 research outputs found
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
An Empirical Study of a Repeatable Method for Reengineering Procedural Software Systems to Object- Oriented Systems
This paper describes a repeatable method for reengineering a procedural
system to an object-oriented system. The method uses coupling metrics to assist a domain
expert in identifying candidate objects. An application of the method to a simple program
is given, and the effectiveness of the various coupling metrics are discussed. We perform
a detailed comparison of our repeatable method with an ad hoc, manual reengineering
effort based on the same procedural program. The repeatable method was found to be
effective for identifying objects. It produced code that was much smaller, more efficient,
and passed more regression tests than the ad hoc method. Analysis of object-oriented
metrics indicated both simpler code and less variability among classes for the repeatable
method
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