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    Survey on distributed word embeddings based on neural network language models
    YU Ke-ren, FU Yun-bin, DONG Qi-wen
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 52-65,79.   DOI: 10.3969/j.issn.1000-5641.2017.05.006
    Abstract396)   HTML20)    PDF (515KB)(1353)      
    Distributed word embedding is one of the most important research topics in the field of Natural Language Processing, whose core idea is using lower dimensional vectors to represent words in text. There are many ways to generate such vectors, among which the methods based on neural network language models perform best. And the respective case is Word2vec, which is an open source tool developed by Google inc. in 2012. Distributed word embeddings can be used to solve many Natural Language Processing tasks such as text clusting, named entity tagging, part of speech analysing and so on. Distributed word embeddings rely heavily on the performance of the neural network language model it based on and the specific task it processes. This paper gives an overview of the distributed word embeddings based on neural network and can be summarized from three aspects, including the construction of classical neural network language models, the optimization method for multi-classification problem in language model, and how to use auxiliary structure to train word embeddings.
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    The auto-question answering system based on convolution neural network
    JING Li-jiao, FU Yun-bin, DONG Qi-wen
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 66-79.   DOI: 10.3969/j.issn.1000-5641.2017.05.007
    Abstract357)   HTML15)    PDF (707KB)(748)      
    The question-answering is a hot research field in natural language processing, which can give users concise and precise answer to the question presented in natural language and provide the users with more accurate information service. There are two key questions to be solved in the question answering system:one is to realize the semantic representation of natural language question and answer, and the other is to realize the semantic matching learning between question and answer. Convolution neural network is a classic deep network structure which has a strong ability to express semantics in the field of natural language processing in recent years, and is widely used in the field of automatic question and answer. This paper reviews some techniques in the question answering system that is based on the convolution neural network, the paper focuses on the knowledge-based and the text-oriented Q&A techniques from the two main perspectives of semantic representation and semantic matching, and indicates the current research difficulties.
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    Study of click through rate prediction in online advertisement
    XIAO YAO, BI Jun-fang, HAN YI, DONG Qi-wen
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 80-86,100.   DOI: 10.3969/j.issn.1000-5641.2017.05.008
    Abstract583)   HTML24)    PDF (548KB)(590)      
    With the development of the Internet and the growth of users, the advertising industry originated from the traditional offline advertising model, is gradually transforming into online advertising model. At the same time, due to the use of large data analysis technology, online advertising shows great advantages when compared with traditional advertising. The advertisers deliver their advertisements to the platform's specific positions by competition auction of counterparts. Therefore, it is important to predict the click through rate (CTR) of a given advertisement before auction, which is important for advertisers to reduce costs and expand their likely revenue.This paper introduces the commonly used ad click rate prediction model, uses the information from different advertisers, advertisements and media platforms as the features of machine learning, and uses real data sets to illustrate the advantages of various models,and the impact of different features on the ad click rate.
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    Smart meter:Privacy-preserving power request scheme
    TIAN Xiu-xia, LI Li-sha, ZHAO Chuan-qiang, TIAN Fu-liang, SONG Qian
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 87-100.   DOI: 10.3969/j.issn.1000-5641.2017.05.009
    Abstract348)   HTML12)    PDF (781KB)(523)      
    A privacy-preserving power request scheme was proposed. The proposed scheme combined Shamir (t,n) threshold secret sharing scheme with Laplace noise perturbation algorithm effectively to achieve paying TOU billing as well as protecting user privacy. Experiments were performed from four aspects:analyzing the security quantitatively and determining the optimal threshold t, giving the experiment on efficiency test, verifying the ε-differential privacy by introducing the Laplace noise perturbation and conducting the scheme feasibility comparison. Experimental results show that the proposed scheme is effective and feasible.
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