Abstract
The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using medical reference texts as input and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false, and match), can have customizable length and difficulty, and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.
Highlights
The recent availability of significant text resources for the health domain has opened the door for a new research direction in the natural language processing area, namely the adaptation of existing technologies and resources to the particularities of the medical domain
The generation of questions from text identified in an image is based on optical character recognition (OCR)
Tesseract 4.0 adds a new OCR engine based on long short-term memory (LSTM) networks [17]
Summary
The recent availability of significant text resources for the health domain has opened the door for a new research direction in the natural language processing area, namely the adaptation of existing technologies and resources to the particularities of the medical domain. Org/courses?languages=en&query=medicine)) or even specialized Wikipedia (Wikipedia Medicine section: https://en.wikipedia.org/wiki/Medicine) articles and various references In this context, it becomes possible to adapt established techniques, originally developed to be used for the general language, to particular goals specific to this domain. It becomes possible to adapt established techniques, originally developed to be used for the general language, to particular goals specific to this domain Two such methods, concerning the area of Natural Language Processing (NLP), are the development of ontologies and the automatic generation of evaluation tests from supporting resources. The generation of questions from text identified in an image is based on optical character recognition (OCR). Tesseract 4.0 adds a new OCR engine based on long short-term memory (LSTM) networks [17] It has support for English and other additional languages, including Romanian, which is why it was our choice for the system we built.
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