資訊工程學系
Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/60
本系前身「資訊教育學系」成立於民國七十四年，首先招收大學部學生，民國九十年成立資訊工程研究所碩士班，而後於民國九十五年進行系、所調整合併為「資訊工程學系」；並於九十六年成立博士班。本系目前每年約招收大學部四十餘人，碩士班六十餘人，博士班約五人，截至民國一百零四年十一月止，總計現有大學部一百九十多人，碩士班一百二十多人，博士班二十三人，合計學生人數約為三百三十多位。
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Item Item Event Extraction for Gene Regulation Network Using Statistical and Semantic Approaches(2014) 班法; Bamfa CeesayGenic 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.