摘要翻译:
在找到所需内容之前需要多次查询的用户可以从自动搜索助手中受益,该助手提供了对其查询修改策略的反馈。我们提出了一种方法来从搜索日志中了解过去哪些类型的查询修改是有效的,哪些类型的查询修改是无效的。该方法从两个维度分析查询修改:一个传统的基于术语的维度和一个语义维度,为查询增加了链接数据实体。将该方法应用于两个搜索引擎的搜索日志,我们确定了查询修改助手改进搜索的六种机会:常用的修改策略,但往往不会导致满意的结果。
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英文标题:
《Towards an automated query modification assistant》
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作者:
Vera Hollink, Arjen de Vries
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最新提交年份:
2011
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Information Retrieval 信息检索
分类描述:Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引,字典,检索,内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Human-Computer Interaction 人机交互
分类描述:Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料,除了H.5.1,它更有可能以多媒体作为主要学科领域。
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英文摘要:
Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.
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PDF链接:
https://arxiv.org/pdf/1104.0128