计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100119-6.doi: 10.11896/jsjkx.211100119
韦入铭1, 陈若愚1, 李晗1, 刘旭红1,2
WEI Ru-ming1, CHEN Ruo-yu1, LI Han1, LIU Xu-hong1,2
摘要： 传统的技术趋势分析工作需要由经验丰富的从业者完成,涉及到大量的文献调研和分析,工作耗时耗力。针对上述问题,提出一种基于深度学习与文本计量的技术趋势分析模型,设计基于BERT_BiLSTM_CRF模型的领域文献命名实体识别算法,优化BERT的掩码机制。以集成电路领域的新闻和论文为数据集,开展BiLSTM_CRF、BERT_BiGRU_CRF等模型以及文中所提BERT_BiLSTM_CRF*模型的对比研究,研究命名实体识别技术在集成电路等领域的数据识别效果。相比于其他算法,文章所提的领域文献命名实体识别算法在F1值上达到了88.6%,奠定了技术趋势分析的基础。基于知识图谱易表达关联关系的特点,创新性提出知识图谱与文本计量技术结合的方法,并从不同角度以可视化的形式展示技术趋势分析效果,最终辅助从业者开展技术趋势智能分析工作。
中图分类号:
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