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摘要翻译：
本文提出了一种增量式方法，可用于智能系统学习主题上下文的更好描述。该方法从分析主题的简单描述中选择少量术语开始，并使用该描述作为初始搜索上下文。使用这些术语，构建一组查询并将其提交给搜索引擎。新的文档和术语被用来提炼所学的词汇。对大量主题进行的评估表明，在构建查询以检索相关材料时，所学词汇比原始词汇更有效。
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英文标题：
《Learning Better Context Characterizations: An Intelligent Information
Retrieval Approach》
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作者：
Carlos M. Lorenzetti and Ana G. Maguitman
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最新提交年份：
2010
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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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英文摘要：
This paper proposes an incremental method that can be used by an intelligent system to learn better descriptions of a thematic context. The method starts with a small number of terms selected from a simple description of the topic under analysis and uses this description as the initial search context. Using these terms, a set of queries are built and submitted to a search engine. New documents and terms are used to refine the learned vocabulary. Evaluations performed on a large number of topics indicate that the learned vocabulary is much more effective than the original one at the time of constructing queries to retrieve relevant material.
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PDF链接：
https://arxiv.org/pdf/1004.3478
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