Technique for eliminating irrelevant terms in term rewriting for annotated media retrieval

Youngchoon Park, Pan-koo Kim, F. Golshani, S. Panchanathan
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引用次数: 1

Abstract

In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.
用于注释媒体检索的术语重写中消除不相关术语的技术
在本文中,我们提出了一种有效的术语重写技术,该技术可以计算一定程度的术语与领域的相关性。该方法解决了本体集成概念搜索中的问题。这些问题是:(1)本体中预定义的概念类与用户不相关(没有找到目标标注的合适概念类)。(ii)提供给用户的相似概念类太多,用户可能无法为目标标注选择正确的语义类(普通用户不是概念分类专家)。该方法利用语义消歧任务查找给定域的相关术语。词义消歧需要术语间相似性测量和术语频率测量。为了对未观测到的术语频率进行公平建模,采用了贴现和再分配模型。所提出的方法是对我们之前在b[13][14]中提出的工作的补充。我们的方法的鲁棒性通过人类判断测试证明,表明我们的方法允许预测与给定领域相关的精确术语列表(总体正确预测的75%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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