排序方式: 共有111条查询结果,搜索用时 31 毫秒
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有导词义消歧机器学习方法由于需要大量人力进行词义标注,难以适用于大规模词义消歧任务.提出一种避免人工词义标注的无导消歧方法.该方法综合利用WordNet知识库中的多种知识源(包括:词义定义描述、使用实例、结构化语义关系、领域属性等)描述歧义词的词义信息,生成词义的“代表词汇集”和“领域代表词汇集”,结合词汇的词频分布信息和所处的上下文环境进行词义判定.利用通用测试集Senseval 3对6个典型的无导词义消歧方法进行开放实验,该方法取得平均正确率为49.93%的消歧结果. 相似文献
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Kobus Barnard Quanfu Fan Ranjini Swaminathan Anthony Hoogs Roderic Collins Pascale Rondot John Kaufhold 《International Journal of Computer Vision》2008,77(1-3):199-217
We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology
for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark
database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al.,
in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our
data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity
(the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation
approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency
that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation
strategy are all available on-line (kobus.ca//research/data/IJCV_2007).
We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The
algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models.
These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More
specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words.
We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants
of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the
other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior. 相似文献
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基于WSDL-S的轻量级语义Web服务匹配模型 总被引:2,自引:1,他引:1
针对现有大多数基于语义的Web服务发现方法实施难度大,实用性不强的问题,提出了一种基于WSDL-S描述的轻量级语义Web服务匹配模型.该模型最大的特点是只需用户输入简单的服务查询字符串就能自动实现Web服务的匹配和调用.服务匹配经过领域本体匹配和基于WordNet词典的同义词匹配两个步骤,特别是领域本体匹配过程中的学习模块能有效提高系统性能.实验结果表明,该系统性能提高在20%到82%之间. 相似文献
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Web信息检索技术已经在全世界广泛应用,然而,搜索引擎的查全率和查准率却不能够令用户满意,因此提出了一种基于通用本体WordNet的语义层次结构.通过计算和分析查询关键字与本体库的映射达到查询优化的目的.该方法通过建立一个简单的语法树并且索引WordNet,对查询关键字词法特性和本体实例之间语义关联强弱进行扩展和分析,提高了查询关键字到本体概念映射的完整性和准确率,进而帮助搜索引擎对用户的意图作出有效推测.实验表明,该方法可以有效地优化查询. 相似文献
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检索系统可以通过引入本体来弥补传统关键词检索语义匮乏的缺陷,然而,领域专家构建本体存在过程复杂、工期长、更新困难等弊端.为此,综合分析多种本体构建方法和技术,针对专利数据的特点给出一套半自动构建本体的方案,在此基础上提出基于半自动构建本体的专利信息检索系统的体系框架,描述系统原型的设计思想和检索流程,通过实验验证该系统能很好的扩充延伸检索词,明显地提高了检索效率以及查全率. 相似文献