Content of Semantic Extraction from Data in our journal

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    Approaches on network vertex embedding
    ZHOU Xiaoxu, LIU Yingfeng, FU Yingnan, ZHU Renyu, GAO Ming
    Journal of East China Normal University(Natural Science)    2020, 2020 (5): 83-94.   DOI: 10.3969/j.issn.1000-5641.202091007
    Abstract （366）   HTML （31）    PDF （1220KB）（194）      
    Network is a commonly used data structure, which is widely applied in social network, communication and biological fields. Thus, how to represent network vertices is one of the difficult problems that is widely concerned in academia and industry. Network vertex representation aims at learning to map each vertex into a vector in a low-dimensional space, and simultaneously preserving the topology structure between vertices in the network. Based on the analysis of the motivation and challenges of network vertex representation, this paper analyzes and compares the mainstream methods of network vertex representation in detail, including matrix decomposition, random walk and deep learning based approaches, and finally introduces the methods to measure the performance of network vertex representation.
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    Approaches for semantic textual similarity
    HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming
    Journal of East China Normal University(Natural Science)    2020, 2020 (5): 95-112.   DOI: 10.3969/j.issn.1000-5641.202091011
    Abstract （845）   HTML （56）    PDF （855KB）（743）      
    This paper summarizes the latest research progress on semantic textual similarity calculation methods, including string-based, statistics-based, knowledge-based, and deep-learning-based methods. For each method, the paper reviews not only typical models and approaches, but also discusses the respective advantages and disadvantages of each routine; the paper also explores public datasets and evaluation metrics commonly used. Finally, we put forward several possible directions for future research in the field of semantic textual similarity.
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    Relation extraction via distant supervision technology
    WANG Jianing, HE Yi, ZHU Renyu, LIU Tingting, GAO Ming
    Journal of East China Normal University(Natural Science)    2020, 2020 (5): 113-130.   DOI: 10.3969/j.issn.1000-5641.202091006
    Abstract （1042）   HTML （55）    PDF （902KB）（679）      
    Relation extraction is one of the classic natural language processing tasks that has been widely used in knowledge graph construction and completion, knowledge base question answering, and text summarization. It aims to extract the semantic relation from a target entity pair. In order to construct a large-scale supervised corpus efficiently, a distant supervision method was proposed to realize automatic annotation by aligning the text with the existing knowledge base. However, it highlights a series of challenges as a result of over-strong assumptions and, accordingly, has attracted the attention of researchers. Firstly, this paper introduces the theories of distant supervision relation extraction and the corresponding formal descriptions. Secondly, we systematically analyze related methods and their respective pros and cons from three perspectives: noisy data, insufficient information, and data imbalance. Next, we explain and compare some benchmark corpus and evaluation metrics. Lastly, we highlight new subsequent challenges for distant supervision relation extraction and discuss trends and directions of future research before concluding.
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