http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
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- 저자 : ( Jieren Cheng ) (검색결과 5 건)
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A review of Chinese named entity recognition
( Jieren Cheng ),( Jingxin Liu ),( Xinbin Xu ),( Dongwan Xia ),( Le Liu ),( Victor S. Sheng ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.
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Generative Adversarial Networks: A Literature Review
( Jieren Cheng ),( Yue Yang ),( Xiangyan Tang ),( Naixue Xiong ),( Yuan Zhang ),( Feifei Lei ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.12
The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of “generative” and “adversarial”, researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.
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Design and Implementation of Teaching System for Mobile Cross-platform
Zhaohua Zheng,Jieren Cheng,Jinlian Peng 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.2
With the development of mobile intelligent terminal, the mobile learning has become an efficient way to learn. However, there are a few work related to the mobile learning system construction in current China. This paper proposed a novel method based mobile cross-platform to construct the learning system, which can be used for teachers to teach and students to learn whenever and wherever. To meet the demand of the cross-platform and multi-terminal, we use the mobile Internet, the HTML5, the Responsive Web Design technology, and the MVC technology architecture. Experimental results show that the proposed method can work effectively and can be widely used in the teaching system.
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MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition
Jingxin Liu,Jieren Cheng,Xin Peng,Zeli Zhao,Xiangyan Tang,Victor S. Sheng 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.6
Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.
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Five new sesquiterpene lactones from Inula hupehensis
Jie Ren,Jiang Jiang Qin,Xiangrong Cheng,Shi Kai Yan,Hui Zi Jin,Wei Dong Zhang 대한약학회 2013 Archives of Pharmacal Research Vol.36 No.11
Four new pseudoguaianolides (1–4), one newguaianolide (5), together with ten known compounds(6–15) were isolated from the aerial parts of Inula hupehensis. Their structures were elucidated mainly on the basisof 1D and 2D spectroscopic methods and circular dichroismanalysis. In addition, compounds 1–10 and 13 weretested for their inhibitory effects against LPS-induced NOproduction in RAW264.7 macrophages. Compounds 2, 6, 8and 9 exhibited significant inhibitory activities with IC50values in the range of 0.6–6.6 lM.
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