• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Large-Scale Knowledge Resources: Construction and Application >Capturing the Structures in Association Knowledge:Application of Network Analyses to Large-Scale Databases of Japanese Word Associations
【24h】

Capturing the Structures in Association Knowledge:Application of Network Analyses to Large-Scale Databases of Japanese Word Associations

机译：捕获联想知识中的结构：网络分析在日语单词联想的大型数据库中的应用

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

Within the general enterprise of probing into the complexities of lexical knowledge, one particularly promising research focus is on word association knowledge. Given Deese's and Cramer's convictions that word association closely mirror the structured patterns of relations that exist among concepts, as largely echoed Hirst's more recent comments about the close relationships between lexicons and ontologies, as well as Firth's remarks about finding a word's meaning in the company it keeps, efforts to capture and unravel the rich networks of associations that connect words together are likely to yield interesting insights into the nature of lexical knowledge. Adopting such an approach, this paper applies a range of network analysis techniques in order to investigate the characteristics of network representations of word association knowledge in Japanese. Specifically, two separate association networks are constructed from two different large-scale databases of Japanese word associations: the Associative Concept Dictionary (ACD) by Okamoto and Ishizaki and the Japanese Word Association Database (JWAD) by Joyce. Results of basic statistical analyses of the association networks indicate that both are scale-free with small-world properties and that both exhibit hierarchical organization. As effective methods of discerning associative structures with networks, some graph clustering algorithms are also applied. In addition to the basic Markov Clustering algorithm proposed by van Dongen, the present study also employs a recently proposed combination of the enhanced Recurrent Markov Cluster algorithm (RMCL) with an index of modularity . Clustering results show that the RMCL and modularity combination provides effective control over cluster sizes. The results also demonstrate the effectiveness of graph clustering approaches to capturing the structures within large-scale association knowledge resources, such as the two constructed networks of Japanese word associations.
机译：在探讨词汇知识的复杂性的一般企业中，一项特别有前途的研究重点是单词联想知识。鉴于Deese和Cramer的信念，即单词联想紧密地反映了概念之间存在的结构化关系模式，这在很大程度上呼应了Hirst最近关于词典与本体之间紧密关系的评论，以及Firth关于在公司中查找单词含义的言论。不断地，捕获和解开将单词连接在一起的丰富联想网络的努力很可能会产生关于词汇知识本质的有趣见解。通过这种方法，本文运用了一系列网络分析技术，以研究日语中单词联想知识的网络表示特征。具体而言，从两个不同的日语单词关联的大型数据库中构建了两个单独的关联网络：冈本和石崎制作的“关联概念词典”（ACD）和乔伊斯创建的“日语单词关联数据库”（JWAD）。关联网络的基本统计分析结果表明，两者都是无标度的，具有小世界属性，并且都表现出层次结构。作为识别与网络关联结构的有效方法，还应用了一些图聚类算法。除了van Dongen提出的基本Markov聚类算法外，本研究还采用了最近提出的增强的递归Markov聚类算法（RMCL）与模块化指标的组合。聚类结果表明，RMCL和模块化组合可有效控制聚类大小。结果还证明了图聚类方法在大规模关联知识资源（例如两个日语单词关联网络）中捕获结构的有效性。

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