处理稀疏文档和主题表示：CHiC 2012实验报告
Dealing with Sparse Document and Topic Representations: Lab Report for CHiC 2012
摘要：
我们将报告GESIS参加第一届CHiC研讨会（CLEF中的文化遗产）的情况。第一次举行，没有新数据集的经验，Europeana的文档转储与ca。有2300万份文件。这个测试集的预测试中出现的最突出的问题是非常不具体的主题和稀疏的文档表示。只有一半的主题（26/50）包含描述，标题通常很短，只有两个词左右。因此，我们专注于三种不同的术语建议和查询扩展机制，以超越稀疏的主题描述。我们使用了两种方法，建立在维基百科的概念提取和方法，应用共现统计可用的Europeana语料库。在下文中，我们将介绍这些方法和评估的初步结果。
英文摘要：
We will report on the participation of GESIS at the first CHiC workshop (Cultural Heritage in CLEF). Being held for the first time, no prior experience with the new data set, a document dump of Europeana with ca. 23 million documents, exists. The most prominent issues that arose from pretests with this test collection were the very unspecific topics and sparse document representations. Only half of the topics (26/50) contained a description and the titles were usually short with just around two words. Therefore we focused on three different term suggestion and query expansion mechanisms to surpass the sparse topical description. We used two methods that build on concept extraction from Wikipedia and on a method that applied co-occurrence statistics on the available Europeana corpus. In the following paper we will present the approaches and preliminary results from their assessments.
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