EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS

Writing a well-structured scientific documents, such as articles and theses, is vital for comprehending the document's argumentation and understanding its messages. Furthermore, it has an impact on the efficiency and time required for studying the document. Proper document segmentation also yields better results when employing automated Natural Language Processing (NLP) manipulation algorithms, including summarization and other information retrieval and analysis functions. Unfortunately, inexperienced writers, such as young researchers and graduate students, often struggle to produce well-structured professional documents. Their writing frequently exhibits improper segmentations or lacks semantically coherent segments, a phenomenon referred to as "mal-segmentation." Examples of mal-segmentation include improper paragraph or section divisions and unsmooth transitions between sentences and paragraphs. This research addresses the issue of mal-segmentation in scientific writing by introducing an automated method for detecting mal-segmentations, and utilizing Sentence Bidirectional Encoder Representations from Transformers (sBERT) as an encoding mechanism. The experimental results section shows a promising results for the detection of mal-segmentation using the sBERT technique

Smart e-Learning: A greater perspective; from the fourth to the fifth generation e-learning

Distance learning has gone through four generations over more than a century. Those four generations, though have elevated the level of interaction between the student and his distant instructor and classmates, are still lacking an essential component for effective teaching, namely customizing the delivery of a course in terms of the material and the style of teaching according to the student profile. In traditional classrooms, the human teacher utilizes his experience and intelligence to adapt the teaching method and style to meet the average student in the classroom. This research has focused on improving the effectiveness and quality of web-based e-learning through adapting the course authoring and delivery to match each individual student skills and preferences. In this article, we shed lights on the vision and status of the eight-year Smart e-Learning environment project: The main objective of this project is to employ AI techniques to advance e-learning forward towards the fifth generation e-learning as we envision it. The idea is to embed instructional design theories as well as learning and cognition theories into e-learning environments to provide a more intelligent and, hence, more effective one-to-one e-learning environments. This article only gives a high level overview; however, the more interested reader will be referred to articles describing the work in more technical details

EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS

Writing a well-structured scientific documents, such as articles and theses, is vital for comprehending the document's argumentation and understanding its messages. Furthermore, it has an impact on the efficiency and time required for studying the document. Proper document segmentation also yields better results when employing automated Natural Language Processing (NLP) manipulation algorithms, including summarization and other information retrieval and analysis functions. Unfortunately, inexperienced writers, such as young researchers and graduate students, often struggle to produce well-structured professional documents. Their writing frequently exhibits improper segmentations or lacks semantically coherent segments, a phenomenon referred to as "mal-segmentation." Examples of mal-segmentation include improper paragraph or section divisions and unsmooth transitions between sentences and paragraphs. This research addresses the issue of mal-segmentation in scientific writing by introducing an automated method for detecting mal-segmentations, and utilizing Sentence Bidirectional Encoder Representations from Transformers (sBERT) as an encoding mechanism. The experimental results section shows a promising results for the detection of mal-segmentation using the sBERT technique