Education plays an important role in building vocabulary knowledge for individual learners and their reading comprehension skills in understanding semantically related texts. For this reason, it is assumed that university students have sufficient vocabulary and reading skills. However, there is evidence that many university students struggle to understand their course content. In the area of text wikification, facilitating the reading comprehension process concentrates on generating hypertexts. Such hypertexts link technical terms found in document contents to Wikipedia. This leads students away from the documents thereby, disrupting the reading comprehension process. This study was based on the need of facilitating reading comprehension without linking away from the content of documents read by university students. A document enrichment approach (TermPedia) was used for this purpose. TermPedia facilitates reading comprehension by providing in-text definitions and background information for technical terms. It integrates four algorithms based on human language technologies: term prediction, term sense disambiguation, term definition and hypertext generation. The algorithms where tested for their performance and TermPedia was evaluated. Test results indicated that the algorithms have a strong potential in providing contextually relevant information for technical terms. Evaluation results show that TermPedia can be useful and usable in facilitating reading comprehension.
|Qualification||Doctor of Philosophy|
|Place of Publication||[Groningen]|
|Publication status||Published - 2020|