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Register a new userPlagiarism Detection in Medical Research Using Medical Ontology
كشف الانتحال في الابحاث الطبية باستخدام الأنطولوجيات الطبية
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يقدم هذا البحث دراسة مرجعية عن الخوارزميات و الأنظمة المتوفرة لكشف الانتحال ، و يقوم بتصميم و بناء تطبيق لكشف الانتحال في الأبحاث الطبية بتوظيف الأنطولوجيات الطبية العالمية المتوفرة على الشبكة العنكبوتية . إن مسألة كشف الانتحال في الأبحاث الطبية المكتوبة باللغات الطبيعية هي مسألة معقدة و تتعلق بالمجال الدقيق للابحاث الطبية . يوجد العديد من الخوارزميات المستخدمة لكشف الانتحال في اللغات الطبيعية و التي تقسم بشكل عام إلى صنفين رئيسين هما خوارزميات المقارنة بين الملفات عن طريق بصمات الملفات ، و خوارزميات مقارنة محتوى الملفات و التي تتضمن خوارزميات مقارنة السلاسل النصية و خوارزميات مقارنة البنى الشجرية للملفات . حديثا تم البحث في مجال خوارزميات كشف الانتحال ذات البعد الدلالي فتم تطوير خوارزميات كشف الانتحال الدلالية المعتمدة على تحليل نماذج الاقتباس في الأبحاث العلمية . تمَ في هذا العمل تطوير نظام لكشف الانتحال باستخدام محرك البحث Bing ، حيث تم استخدام خوارزمية تعتمد على استخدام و توظيف نوعين من الانطولوجيات و هي الأنطولوجيات العامة مثل وورد نت ( WordNet ) و الأنطلوجيات الطبية العالمية أشهرها أنطولوجيا الأمراض Diseases ontology التي تحتوي على توصيف الأمراض و خصائصها و تعريفها و اشتقاق الأمراض من بعضها.
This paper presents a reference study of available algorithms for plagiarism detection and it develops semantic plagiarism detection algorithm for plagiarism detection in medical research papers by employing the Medical Ontologies available on the World Wide Web. The issue of plagiarism detection in medical research written in natural languages is a complex issue and related exact domain of medical research. There are many used algorithms for plagiarism detection in natural language, which are generally divided into two main categories, the first one is comparison algorithms between files by using fingerprints of files, and files content comparison algorithms, which include strings matching algorithms and text and tree matching algorithms. Recently a lot of research in the field of semantic plagiarism detection algorithms and semantic plagiarism detection algorithms were developed basing of citation analysis models in scientific research. In this research a system for plagiarism detection was developed using “Bing” search engine, where tow type of ontologies used in this system, public ontology as wordNet and many standard international ontologies in medical domain as Diseases ontology which contains a descriptions about diseases and definitions of it and the derivation between diseases.
References used
Vinod K.R.،Sandhya.S،Sathish Kumar D،Harani A،David Banji and،Otilia JF Banji 2011 ، Plagiarism history ،detection and prevention, Hygeia, Vol.3-Issue.1-Page 1- 4
Maxim mozgovoy, enhancing computer-aided plagiarism , university of joensuu computer science and statistics dissertations 18
Schleimer, S., Wilkerson, D. S., & Aiken, A. (2003). Winnowing: Local Algorithms for Document Fingerprinting. Proceedings of the 2003 ACM SIGMOD International Conference on on Management of Data - SIGMOD ’03, 76–85
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This paper presents a review of available algorithms and plagiarism detection systems، and an
implementation of Plagiarism Detection System using available search engines on the web.
Plagiarism detection in natural language documents is a complicat
ed problem and it is related to the
characteristics of the language itself.
There are many available algorithms for plagiarism detection in natural languages .Generally these
algorithms belong to two main categories ; the first one is plagiarism detection algorithms based on
fingerprint and the second is plagiarism detection algorithms based on content comparison and includes
string matching and tree matching algorithms .
Usually available systems of plagiarism detection use specific type of detection algorithms or use a
mixture of detection algorithms to achieve effective detection systems (fast and accurate).
In this research, a plagiarism detection system has been developed using Bing search engine and a
plagiarism detection algorithm based on Rhetorical Structure Theory.
In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes. Such is the task of assertion detection - to
identify the assertion class (present, possible, absent) of an entity based on textual cues in unstructured text. We evaluate state-of-the-art medical language models on the task and show that they outperform the baselines in all three classes. As transferability is especially important in the medical domain we further study how the best performing model behaves on unseen data from two other medical datasets. For this purpose we introduce a newly annotated set of 5,000 assertions for the publicly available MIMIC-III dataset. We conclude with an error analysis that reveals situations in which the models still go wrong and points towards future research directions.
In this paper we review and list, the advantages and limitations of the significant
effective techniques employed or developed in text plagiarism detection. It was found that
many of the proposed methods for plagiarism detection have a weakness poi
nts and do not
detect some types of plagiarized operations.
This paper show a survey about plagiarism detection including several important
subjects in plagiarism detection, which is plagiarism definition, plagiarism prevention and
detection, plagiarism detection systems, plagiarism detection processes and some of the
current plagiarism detection techniques.
This paper compares between different plagiarism detection algorithms, and shows
the points of weakness, and points of efficiency, and describe the power of semantic
plagiarism detection methods, and shows its efficiency in detect plagiarism cases that
another plagiarism detection algorithms don’t able to detect these cases, that semantic
plagiarism detection methods are developed to get rid of traditional weakness points for all
plagiarism detection methods have.
We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplificati
on does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing jargon'' terms; we find that this yields improvements over baselines in terms of readability.
Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical
conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
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