我国人工智能政策新词发现与演化研究——一个多特征融合的算法

Discovery and Evolution of New Words in Chinese Artificial Intelligence Policies——A Multi-feature Fusion Algorithm

[目的/意义]作为中文分词的基础研究,新词发现是研究政策创新和扩散的重要技术方法。本文通过改进新词发现算法优化了政策文本分词不准确的问题,并构建词库以支持人工智能政策的演化研究。[方法/过程]提出多特征融合新词发现算法MFF,实现了对人工智能政策新词的挖掘,从新词角度对人工智能政策的创新、延续和扩散进行演化分析。[结果/结论]实验结果证明,本文提出的多特征融合新词发现算法MFF能够有效提升分词效果,丰富领域词库;人工智能政策新词出现的时序变化反映了不同阶段政策关注的重点发展领域,揭示了中央和地方政府在政策创新、延续、扩散和演化方面的特点。

[Purpose/Significance]New word discovery,as the basic research of Chinese word segmentation,is very important for policy text mining,analysis and knowledge discovery.This paper intends to optimize the new word discovery algorithm and improve word segmentation performance in the AI policies.And we construct a domain lexicon to support the evolution study of AI policies.[Method/Process]A multi-feature fusion new word discovery algorithm(MFF)was proposed,which realized the mining of new word in the field of AI policies,and analyzed the evolution,continuation and diffusion of AI policies from the perspective of new word.[Result/Conclusion]The experimental results demonstrate the multi-feature fusion new word discovery algorithm effectively improves word segmentation performance and enriches the domain lexicon.The temporal changes in the emergence of AI policies neologisms reflect the key development areas of policy attention at different stages,and reveal the characteristics and evolution process of central and local governments in terms of policy innovation,continuation,diffusion and evolution.