時間：1月7日（星期四）下午14:30-16:00
騰訊會議號：296 459 659
報告內容簡介：
The size of biomedical literature is massive and expands at a fast rate, due to the rapid growth in biomedical research and development. PubMed is an online portal (accessing primarily the MEDLINE database) that include more than 30 million of research articles (abstracts) on life sciences and biomedical topics by the end of January 2020. Biomedical literature provides healthcare practitioners (e.g., physicians, pharmacists) up-to-date biomedical research findings, which can be applied to improve professional practices and healthcare outcomes. Moreover, biomedical literature is core to new knowledge creation and discovery. Because the size of biomedical literature expands rapidly, manual review and inspection of biomedical research articles is very difficult and time-consuming. As a result, the development of some natural language understanding (NLU) techniques that can comprehend or extract important information from this huge collection of literature is essential and desirable.
One important type of information that can be extracted from these articles are biomedical relations discussed in each article. Examples of biomedical relations include drug-disease relations, chemical-protein relations, gene-disease relations, protein interactions, drug-drug interactions, etc. Formally, given a sentence (or a small segment of text) that contains two entities of interest, the task of relation extraction is to predict whether the sentence describes some relation (out of a predefined set of relation types) between the two entities and, if so, to classify which relation class does the sentence point to. In this talk, I will present our proposed biomedical relation extraction methods that follow the deep-learning-based approach. In addition, in this talk, I will also discuss an important application of biomedical relation extraction, i.e., literature-based drug repurposing.
報告人簡介：
魏志平教授目前任職于臺灣大學信息管理系，擔任特聘教授。魏教授為美國亞歷桑那大學管理信息系統博士(1996年畢業)，曾于清華大學以及中山大學任教，亦曾于美國華盛頓大學、美國伊利諾大學香檳分校、香港中文大學擔任訪問學者。
魏教授主要研究領域為大數據分析、文字探勘、社群媒體分析、生醫信息、專利分析與探勘等，其研究成果發表于信息管理或信息科技相關領域之國際知名期刊中，例如 Journal of Management Information Systems (JMIS) 、 European Journal of Information Systems (EJIS) 、 Decision Sciences 、 Decision Support Systems (DSS) 、 Information & Management (I&M) 、 Journal of the Association for Information Science and Technology 、 IEEE Transactions in Engineering Management , IEEE Transactions on Systems, Man, and Cybernetics 、 IEEE Intelligent Systems 、 IEEE Transactions on Information Technology in Biomedicine 等。
（承辦：管理工程系、科研與學術交流中心）
