• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge and information systems >Measuring similarity and relatedness using multiple semantic relations in WordNet
【24h】

Measuring similarity and relatedness using multiple semantic relations in WordNet

机译：使用Wordnet中的多语义关系测量相似性和相关性

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

Semantic similarity and relatedness computation has attracted an increasing amount of attention among researchers. The majority of previous studies, including edge-based and information content-based methods, rely on a single semantic relationship in WordNet such as the “is-a” relation. However, a performance ceiling may have been created by semantic unicity and inadequate calculation in solely “is-a” relation-based measurements, i.e., the computed results for some word pairs are too small and significantly deviate from human judgments. For this problem, we propose the following solutions: (1) We introduce the notion of the nearest common descendant to provide a supplement for commonalities between concepts according to genetics theory. (2) We design various targeted methods for different incomplete semantic relations. Therefore, various semantic relations can participate in similarity and relatedness computations in their most appropriate manners. (3) We utilize the cross-use of incomplete semantic relations similar-to and antonymy to solve the challenge of adjective and adverb similarity/relatedness measurements in WordNet. (4) We propose a targeted independent computation and largest contribution aggregation method to break through the performance ceiling of similarity/relatedness measurements based on single “is-a” relations. We conduct evaluations of our proposed model using seven extensively employed datasets. These evaluations indicate that our method significantly improves the performance of the existing methods based on single “is-a” relations. Their best Pearson coefficient with human judgments on both the MC30 and RG65 is increased to 0.9. With the development and enrichment of semantic relations in WordNet, our proposed model can be expected to have a more prominent role.
机译：语义相似性和相关性计算引起了研究人员之间的注意力越来越大。以前的大多数研究，包括基于边缘和基于信息的基于信息的方法，依赖于Wordnet中的单个语义关系，例如“是”关系。然而，可以通过语义单性和基于关系的基于关系的基于关系的计算不足，即“是基于关系的计算，即，一些字对对的计算结果太小并且显着偏离了人类判断。对于这个问题，我们提出以下解决方案：（1）我们介绍了最接近的公共后代的概念，以便根据遗传学理论为概念之间的共性提供补充。 （2）我们设计各种针对不同的非完全语义关系的方法。因此，各种语义关系可以以最合适的方式参与相似性和相关性计算。 （3）我们利用不完全语义关系的交叉使用类似于和反义来解决Wordnet中的形容词和副词的相似性/相关性测量的挑战。 （4）我们提出了一个针对性的独立计算和最大的贡献聚集方法来突破基于单一“是”关系的相似性/相关性测量的性能天花板。我们使用七个广泛使用的数据集进行我们提出的模型的评估。这些评估表明，我们的方法显着提高了基于单一“是”关系的现有方法的性能。他们的最佳Pearson系数与MC30和RG65两者的人类判断增加到0.9。随着Wordnet中的语义关系的发展和富集，我们提出的模型可以预期具有更突出的作用。

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