![]() ![]() To demonstrate the benefit of our benchmark, we conduct an evaluation, with two objectives. Finally, based on this, we propose concrete benchmark queries. Using this notion, we formulate queries in line with the information needs of users. ![]() We then formalize the notion of word context to facilitate the analysis of specific concepts. In this article, we propose such a benchmark, with the following innovations: As a first step, we collect and structure various information needs of the target users. But this currently is unclear, and there is no benchmark to evaluate distant reading systems. Making such systems efficient calls for a specification of the necessary functionality and clear expectations regarding typical work loads. Studying the meaning of words using large corpora requires efficient systems for text analysis, so-called distant reading systems. ![]() ![]() An important objective is to study the ideas and expectations of a society regarding specific concepts, like “freedom” or “democracy,” both for today’s society and even more for societies of the past. The data studied by scientists in the humanities include large textual corpora. Data science deals with the discovery of information from large volumes of data. ![]()
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