[Objective] This paper propose a fine-grained multi-dimensional analysis framework based on multi-source data and in-depth semantic contents, aiming to address the deficiencies in analyzing U.S. export controls.[Methods] We constrcuted the framework based on the concept of multi-source data fusion, which integrated data from the CCL for items, the EAR for regulations, the blacklist for entities, and the Federal Register for polices. First, we identified the technical terms, the exact technical indicators values and the relationship between the controlled items from the multi-source data. Then, we built an index using the semantic dictionary and model. Third, we used the named entity recognition method to establish the correlated relationship between the controlled items and entities. This framework contains four analysis modes for the status quo, the specific items, the time sequences, and the countries.[Results] We examined the effectiveness of the framework with an empirical study on lithography. The recall for recognizing the controlled items reached 97.3% with the same tail ECCN number. The precision of recognizing Chinese mainland’s entity domains was up to 83.8%.[Limitations] We only selected the lithography for the empirical study and the framework could be improved.[Conclusions] The proposed framework provides an effective method to analyze the texts of U.S. export control documents.
李广建,王锴,张庆芝. 基于多源数据的美国出口管制分析框架及其实证研究*[J]. 数据分析与知识发现, 2020, 4(9): 26-40.
Li Guangjian,Wang Kai,Zhang Qingzhi. Analysis Framework Based on Multi-Source Data for US Export Control: An Empirical Study. Data Analysis and Knowledge Discovery, 2020, 4(9): 26-40.
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