Abstract
Reading is one of the essential practices of modern human learning. Comprehending prose text simply from the available text is particularly challenging as in general the comprehension of prose requires the use of external knowledge or references. Although the processes of reading comprehension have been widely studied in the field of psychology, no algorithm level models for comprehension have yet to be developed. This paper has proposed a comprehension engine consisting of knowledge induction which connects the knowledge space by augmenting associations within it. The connections are achieved through the automatic incremental reading of external references and the capturing of high familiarity knowledge associations between prose concepts. The Ontology Engine is used to find lexical knowledge associations amongst concept pairs, with the objective being to obtain a knowledge space graph with a single giant component to establish a base model for prose comprehension. The comprehension engine is evaluated through experiments with various selected prose texts. Akin to human readers, it could mine reference texts from modern knowledge corpuses such as Wikipedia and WordNet. The results demonstrate the potential efficiency of using the comprehension engine that enhances the quality of reading comprehension in addition to reducing reading time. This comprehension engine is considered the first algorithm level model for comprehension compared with existing works.
Highlights
Text comprehension is a form of knowledge acquisition whereby readers interact with text and relate the ideas represented to their knowledge and experiences [1]
Reading a single text does not qualify readers to achieve the required level of comprehension. This is because the comprehension process depends largely on reader knowledge or additional knowledge acquired from external sources
This engine is based on the Knowledge Induction Process which targets increasing knowledge comprehension through two steps: 1) the incremental reading of external reference texts and giving an extractive summary of each by capturing of the highest familiarity knowledge associations amongst prose concepts
Summary
Text comprehension is a form of knowledge acquisition whereby readers interact with text and relate the ideas represented to their knowledge and experiences [1]. While the mental process behind such knowledge induction is intriguing, adopting computational algorithms can help more effective reading comprehension of prose by automatically capturing relevant text pieces from external references and relevant knowledge association of the prose concepts as a summary. The proposed comprehension engine contributes to the reading rate that readers may need to acquire specific knowledge from reference texts. This engine is based on the Knowledge Induction Process which targets increasing knowledge comprehension through two steps: 1) the incremental reading of external reference texts and giving an extractive summary of each by capturing of the highest familiarity knowledge associations amongst prose concepts.
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