计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 214-220.doi: 10.11896/j.issn.1002-137X.2019.05.033
凡子威, 张民, 李正华
FAN Zi-wei, ZHANG Min, LI Zheng-hua
摘要: 隐式篇章关系分类是浅层篇章结构分析(Shallow Discourse Parsing)中的子任务,也是自然语言处理(Natural Language Processing,NLP)中的一项重要任务。隐式篇章关系是由篇章关系中的论元对推理出来的逻辑语义关系。隐式篇章关系的分析结果可以应用于许多自然语言处理任务中,如机器翻译、自动文档摘要、问答系统等。针对隐式篇章关系分类任务,提出一种基于自注意力机制和句法信息的方法。通过双向长短时记忆网络(Bidirectional Long Short-Term Memory Network)对输入的结合句法信息的论元对进行建模,将论元对表示成低维稠密的向量;通过自注意力机制对论元对信息进行筛选。在PDTB2.0 数据集上进行实验,结果表明该方法较基准系统获得了更好的效果。
中图分类号:
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