• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ACM transactions on Asian language information processing >Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via a Mutual Learning Method
【24h】

Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via a Mutual Learning Method

机译：利用层级深度语义通过相互学习方法对内隐语篇关系进行分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This article presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English. With the absence of explicit discourse markers, traditional discourse techniques mainly concentrate on discrete linguistic features in this task, which always leads to a data sparseness problem. To relieve this problem, we propose a mutual learning neural model that makes use of multilevel semantic information together, including the distribution of implicit discourse relations, the semantics of arguments, and the co-occurrence of phrases and words. During the training process, the predicting targets of the model, which are the probability of the discourse relation type and the distributed representation of semantic components, are learned jointly and optimized mutually. The experimental results show that this method outperforms the previous works, especially in multiclass identification attributed to the hierarchical semantic representations and the mutual learning strategy.
机译：本文提出了一种使用分层深层语义对英语隐性话语关系进行分类的相互学习方法。在缺乏明确的话语标记的情况下，传统的话语技术在此任务中主要集中在离散的语言特征上，这总是导致数据稀疏性问题。为了缓解这个问题，我们提出了一个相互学习的神经模型，该模型一起使用多级语义信息，包括隐式话语关系的分布，论点的语义以及短语和单词的共现。在训练过程中，共同学习并相互优化模型的预测目标，即话语关系类型的概率和语义成分的分布式表示。实验结果表明，该方法优于以前的工作，特别是在归因于层次语义表示和相互学习策略的多类识别中。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号