理學院

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/3

學院概況

理學院設有數學系、物理學系、化學系、生命科學系、地球科學系、資訊工程學系6個系(均含學士、碩士及博士課程),及科學教育研究所、環境教育研究所、光電科技研究所及海洋環境科技就所4個獨立研究所,另設有生物多樣性國際研究生博士學位學程。全學院專任教師約180人,陣容十分堅強,無論師資、學術長現、社會貢獻與影響力均居全國之首。

特色

理學院位在國立臺灣師範大學分部校區內,座落於臺北市公館,佔地約10公頃,是個小而美的校園,內含國際會議廳、圖書館、實驗室、天文臺等完善設施。

理學院創院已逾六十年,在此堅固基礎上,理學院不僅在基礎科學上有豐碩的表現,更在臺灣許多研究中獨占鰲頭,曾孕育出五位中研院院士。近年來,更致力於跨領域研究,並在應用科技上加強與業界合作,院內教師每年均取得多項專利,所開發之商品廣泛應用於醫、藥、化妝品、食品加工業、農業、環保、資訊、教育產業及日常生活中。

在科學教育研究上,臺灣師大理學院之排名更高居世界第一,此外更有獨步全臺的科學教育中心,該中心就中學科學課程、科學教與學等方面從事研究與推廣服務;是全國人力最充足,設備最完善,具有良好服務品質的中心。

在理學院紮實、多元的研究基礎下,學生可依其性向、興趣做出寬廣之選擇,無論對其未來進入學術研究領域、教育界或工業界工作,均是絕佳選擇。

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    Event Extraction for Gene Regulation Network Using Statistical and Semantic Approaches
    (2014) 班法; Bamfa Ceesay
    Genic regulation networks are the primary study object in systems biology. They allow better understanding of the relationship between molecular mechanisms and cellular behavior. However, one of the bottlenecks in systems biology is the acquisition of an accurate genetic regulation network. In the recent years, the BioNLP community has produced systems for extracting genic interactions and Protein-Protein Interaction (PPI) from the literature. The sporulation network of the bacteria model for bacillus subtilis is very well studied. The automatic design of the gene regulation network is one of the main challenges in biology, because it is a crucial step forward in understanding the cellular regulation system. In this study, we present a description of a system on Gene Regulation Network (GRN) in bacteria and we use the data from the BioNLP’13 shared task (BIONLP-ST) on Event Extraction. For this work, we first propose a procedure to do biological event extraction combining a dependency graph-based method and a method using semantic analysis in Natural Language Processing (NLP). Then a second design, a statistical approach using Hidden Markov Model (HMM), is experimented. Dependency parsing is a significant and commonly used approach to finding out the dependency relationship between tokens in, for example, a sentence. We use dependency features to identify and classify our event trigger tokens using multi–class Support Vector Machine (SVMLight multiclass). However, the dependency features are not sufficient to give the semantic relationship between tokens with a sentence. Therefore, we develop a semantic analysis approach based on NLP techniques to capture more detail information and improve our result on event extraction. In our second design approach, we use a general statistical method via Markov’s logic instead of developing certain inferences and learning algorithms. Markov’s Model has achieved significant recognition in Natural Language Processing especially in the field of speech recognition. Our result shows that the graph-based approach obtains a better result on event extraction and produces a much better regulation network than the semantic analysis method. The combination of the two approaches has yet a much slightly better result than that with the individual approach. Moreover, the proposed statistical approach achieves a much better result than the combined and individual results of our graph-based and semantic analysis approaches.