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    Transfer learning based QA model of FAQ using CQA data
    SHAO Ming-rui, MA Deng-hao, CHEN Yue-guo, QIN Xiong-pai, DU Xiao-yong
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 74-84.   DOI: 10.3969/j.issn.1000-5641.2019.05.006
    Abstract360)   HTML251)    PDF (1735KB)(329)      
    Building an intelligent customer service system based on FAQ (frequent asked questions) is a technique commonly used in industry. Question answering systems based on FAQ offer numerous advantages including stability, reliability, and quality. However, given the practical limitations of scaling a manually annotated knowledge base, models often have limited recognition ability and can easily encounter bottlenecks. In order to address the problem of limited scale with FAQ datasets, this paper offers a solution at both the data level and the model level. At the data level, we use Baidu Knows to crawl relevant data and mine semantically equivalent questions, ensuring the relevance and consistency of the data. At the model level, we propose a deep neural network with transAT oriented transfer learning, which combines a transformer network and an attention network, and is suitable for semantic similarity calculations between sentence pairs. Experiments show that the proposed solution can significantly improve the impact of the model on FAQ datasets and to a certain extent resolve the issues with the limited scale of FAQ datasets.
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    Research on artificial intelligence assisted decision-making algorithms for lawyers based on legal-computing theory
    CHEN Liang, GUO Jia-wen, WU Jian-gong, WANG Zhan-quan, SHI Ling
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 85-99.   DOI: 10.3969/j.issn.1000-5641.2019.05.007
    Abstract412)   HTML196)    PDF (698KB)(271)      
    At present, there is a lack of intelligent decision-making tools applied to legal theory and practice. Given the characteristics of data in this field, we establish an intelligent decision-making algorithm using a variety of data analysis models. Legal-computing is focused on data-based mechanization of legal reasoning. It establishes a relationship between legal research and applications using the characteristics and data features of computer science. On this basis, the method of "implication classification" is formed, the decision tree and Naive Bayes algorithms are improved for application to the legal arena, and a coordinate system of legal relationships is established to transfer traditional legal relationship analysis into a spatial geometric system. Experimental results show that the algorithm is consistent with a lawyer's handling strategy and results, and has the feasibility of assisting lawyers more broadly in decision-making.
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    Optimal route search based on user preferences
    JIANG Qun, DAI Ge-nan, ZHANG Sen, GE You-ming, LIU Yu-bao
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 100-112.   DOI: 10.3969/j.issn.1000-5641.2019.05.008
    Abstract426)   HTML241)    PDF (1712KB)(334)      
    This paper studies the methodology for optimal route search based on user preferences, such as keyword and weight preferences, under a constraint. The research problem is NP-hard. To solve the query efficiently, we propose two new index building methods and select candidate nodes for retrieving the established indices. This paper subsequently proposes an A* based route search algorithm to identify the optimal route and use several effective pruning strategies to speed up execution. Experimental results on two real-world check-in datasets demonstrates the effectiveness of the proposed method. When the budget ranges from 4 hours to 7 hours, our algorithm performs better than the state-of-the-art PACER algorithm.
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    Self-attention based neural networks for product titles compression
    FU Yu, LI You, LIN Yu-ming, ZHOU Ya
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 113-122,167.   DOI: 10.3969/j.issn.1000-5641.2019.05.009
    Abstract440)   HTML257)    PDF (857KB)(376)      
    E-commerce product title compression has received significant attention in recent years, since it can facilitate more specific information for cross-platform knowledge alignment and multi-source data fusion. Product titles usually contain redundant descriptions, which can lead to inconsistencies. In this paper, we propose self-attention based neural networks for this task. Given the fact that self-attention mechanism networks cannot directly capture sequence features of product names, we enhance the mapping networks with a dot-attention structure, which was computed for the query and key-value pairs by a gated recurrent unit (GRU) based recurrent neural network. The proposed method improves the analytical capability of the model at a lower relative computational cost. Based on data from LESD4EC, we built two E-commerce datasets of product core phrases named LESD4EC L and LESD4EC S; we subsequently tested the model on these two datasets. A series of experiments show that the proposed model achieves better performance in product title compression than existing techniques.
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    Electric energy abnormal data detection based on Isolation Forests
    HUANG Fu-xing, ZHOU Guang-shan, DING Hong, ZHANG Luo-ping, QIAN Shu-yun, YUAN Pei-sen
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 123-132.   DOI: 10.3969/j.issn.1000-5641.2019.05.010
    Abstract405)   HTML225)    PDF (612KB)(250)      
    With the development of power information systems, users' requirements for the quality of power data has gradually increased. Hence, it is important to ensure the accuracy, reliability, and integrity of massive power data. In this paper, an anomaly detection algorithm based on Isolation Forests is used to realize anomaly detection of large-scale electric energy data. Isolation Forest algorithms generate random binary trees and isolated forest models by dividing training samples and detecting abnormal data points. The algorithm can not only process massive data quickly, but it also offers accurate results and a high degree of reliability. In this paper, the positive active total power (PAP) and reverse active total power (RAP) fields of large-scale electric energy data are determined. The experimental results show that the algorithm has high detection efficiency and accuracy.
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    Prediction of power network stability based on an adaptive neural network
    ZHAO Bo, TIAN Xiu-xia, LI Can
    Journal of East China Normal University(Natural Sc    2019, 2019 (5): 133-142.   DOI: 10.3969/j.issn.1000-5641.2019.05.011
    Abstract395)   HTML178)    PDF (751KB)(290)      
    The safety and stability of the power grid serves as the basis for reform, development, and stability of power enterprises as well as for broader society. With the increasing complexity of power grid structures, safety and stability of the power grid is important for ensuring the rapid and effective development of the national economy. In this paper, we propose an optimal neural network stability prediction model and compare performance with classical machine learning methods. By analyzing the UCI2018 grid stability simulation dataset, the experimental results show that the proposed method can achieve higher prediction accuracy and provide a new approach for research of power big data.
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