Content of Financial Knowledge Graph in our journal

        Published in last 1 year |  In last 2 years |  In last 3 years |  All
    Please wait a minute...
    For Selected: Toggle Thumbnails
    Joint extraction of entities and relations for domain knowledge graph
    Rui FU, Jianyu LI, Jiahui WANG, Kun YUE, Kuang HU
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 24-36.   DOI: 10.3969/j.issn.1000-5641.2021.05.003
    Abstract704)   HTML70)    PDF (842KB)(571)      

    Extraction of entities and relationships from text data is used to construct and update domain knowledge graphs. In this paper, we propose a method to jointly extract entities and relations by incorporating the concept of active learning; the proposed method addresses problems related to the overlap of vertical domain data and the lack of labeled samples in financial technology domain text data using the traditional approach. First, we select informative samples incrementally as training data sets. Next, we transform the exercise of joint extraction of entities and relations into a sequence labeling problem by labelling the main entities. Finally, we fulfill the joint extraction using the improved BERT-BiGRU-CRF model for construction of a knowledge graph, and thus facilitate financial analysis, investment, and transaction operations based on domain knowledge, thereby reducing investment risks. Experimental results with finance text data shows the effectiveness of our proposed method and verifies that the method can be successfully used to construct financial knowledge graphs.

    Table and Figures | Reference | Related Articles | Metrics
    Data augmentation technology for named entity recognition
    Xiaoqin MA, Xiaohe GUO, Yufeng XUE, Lin YANG, Yuanzhe CHEN
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 14-23.   DOI: 10.3969/j.issn.1000-5641.2021.05.002
    Abstract697)   HTML425)    PDF (689KB)(332)      

    A named entity recognition task is as a task that involves extracting instances of a named entity from continuous natural language text. Named entity recognition plays an important role in information extraction and is closely related to other information extraction tasks. In recent years, deep learning methods have been widely used in named entity recognition tasks; the methods, in fact, have achieved a good performance level. The most common named entity recognition models use sequence tagging, which relies on the availability of a high quality annotation corpus. However, the annotation cost of sequence data is high; this leads to the use of small training sets and, in turn, seriously limits the final performance of named entity recognition models. To enlarge the size of training sets for named entity recognition without increasing the associated labor cost, this paper proposes a data augmentation method for named entity recognition based on EDA, distant supervision, and bootstrap. Using experiments on the FIND-2019 dataset, this paper illustrates that the proposed data augmentation techniques and combinations thereof can significantly improve the overall performance of named entity recognition models.

    Table and Figures | Reference | Related Articles | Metrics
    Research progress in Chinese named entity recognition in the financial field
    Qiurong XU, Peng ZHU, Yifeng LUO, Qiwen DONG
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 1-13.   DOI: 10.3969/j.issn.1000-5641.2021.05.001
    Abstract1059)   HTML656)    PDF (821KB)(486)      

    As one of the basic components of natural language processing, named entity recognition (NER) has been an active area of research both domestically in China and abroad. With the rapid development of financial applications, Chinese NER has improved over time and been applied successfully throughout the financial industry. This paper provides a summary of the current state of research and future development trends for Chinese NER methods in the financial field. Firstly, the paper introduces concepts related to NER and the characteristics of Chinese NER in the financial field. Then, based on the development process, the paper provides an overview of detailed characteristics and typical models for dictionary and rule-based methods, statistical machine learning-based methods, and deep learning-based methods. Next, the paper summarizes public data collection tools, evaluation methods, and applications of Chinese NER in the financial industry. Finally, the paper explores current challenges and future development trends.

    Table and Figures | Reference | Related Articles | Metrics