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Research on Person Entity Extraction from Ancient Sources
Yihong Ma, Qingkai Zeng, Tianwen Jiang, Liang Cai, Meng Jiang
School of Finance Shanghai University of Finance and Economics Shanghai China., University of Notre Dame Notre Dame, IN 46556 USA
Abstract: We in this work have worked for data retrieval from Chinese historiography. The main issue is the low resource of the language: deep learning requires large amounts of annotated data and becomes impracticable when such data is not available. We used the subject experts to curate a set of person entities and their profile attributes and relations from two documents. We introduce a pattern-based bootstrapping approach to extract the information with a very small number (i.e., 1 or 2) of seed patterns. The testing results show the effectiveness as well as the limitations of the iterative method.
Keywords: Information Extraction, Entity Profiling, Classical Chinese, Textual Pattern, Bootstrapping Research on Person Entity Extraction from Ancient Sources
DOI:https://doi.org/10.6025/jes/2020/10/3/102-113
Full_Text   PDF 3.37 MB   Download:   7  times
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