...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer speech and language >Exploring intrinsic information content models for addressing the issues of traditional semantic measures to evaluate verb similarity
【24h】

Exploring intrinsic information content models for addressing the issues of traditional semantic measures to evaluate verb similarity

机译：探索内在信息内容模型，用于解决传统语义措施问题，以评估动词相似度

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 measures play an important role in many natural language processing and information retrieval activities. It is highly challenging to measure semantic similarity with higher accuracy. A notable branch of semantic similarity evaluation based on information content (IC) is popular in this aspect. Intrinsic information content (IIC) models are another wing of IC based evaluation. Both IC based and IIC based approaches majorly handled similarity evaluation of nouns. Research related to semantic similarity assessment of verb pairs are rarely discussed. To bridge this gap, this work examines various IC based, IIC based approaches on verb pairs. A detailed discussion of the existing measures and their drawbacks are mentioned in this work. Strategies based on information content, length and depth of the concepts are discussed and tested on benchmark datasets. Existing intrinsic information content models are enhanced by addressing various issues like (a) dealing concepts with no path in WordNet and (b) handling the synonym sets of verb concepts. Measures based on path length, intrinsic information content, combined strategies and non-linear strategies for verb pairs are thoroughly inspected. This paper also presents novel strategies to understand novel aspects that are not addressed before. The strategies are experimented by generating the synonym sets of required parts-of-speech which proved very effective in improving the correlation with human judgment. Results on benchmark datasets specify that the proposed approaches for verb similarity will be a guiding factor for understanding the natural language processing tasks.
机译：语义相似度测量在许多自然语言处理和信息检索活动中起重要作用。以更高的准确度测量语义相似性是强大的挑战性。基于信息内容（IC）的语义相似性评估的一个值得注意的分支在这方面是流行的。内在信息内容（IIC）模型是基于IC的另一个翼。基于IC和IIC基于IIC的方法主要处理名词的相似性评估。很少讨论与动词对的语义相似性评估相关的研究。为了弥合这个差距，这项工作审查了基于IIC的IIC的IIC方法。本工作中提到了对现有措施及其缺点的详细讨论。基于信息内容，长度和深度的策略在基准数据集中讨论和测试概念。通过解决（a）在WordNet中没有路径的概念和（b）处理动词概念的同义词集的不同之类的各种问题来增强现有的内部信息内容模型彻底检查了基于路径长度，内在信息内容，组合策略和非线性策略的措施。本文还展示了了解以前未解决的新颖方面的新战略。通过生成所需部分的同义词，在提高与人为判断的相关性方面，通过生成所需部分的同义词来进行实验。基准数据集的结果指定了动词相似性的提出方法将是理解自然语言处理任务的指导因素。

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