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Making 1:n explorable: a search interface for the ZAS database of clause-embedding predicates
(2017)
We introduce a recently published corpus-based database of German clause-embedding predicates and present an innovative web application for exploring it. The application displays the predicates and the corpus examples for these predicates in two separate tables that can be browsed and searched in real time. While familiar web interface paradigms make it easy for users to get started, the data presentation and the interactive advanced search components for the two tables are designed to accommodate remarkably complex query needs without the need for resorting to a dedicated query language or a more specialized tool. The 1:n relationship between predicates and their examples is exploited in the two tables in that, e.g. the predicate table also shows, for each predicate and each example attribute, all values that occur in the examples for this predicate. An easy-to-use visual query builder for arbitrary Boolean combinations of search criteria can optionally be displayed to pre-filter the underlying data presented in both tables. Several options for altering quantifier scope can be activated with simple checkboxes and considerably widen the space of searchable constellations.
We start by trying to answer a question that has already been asked by de Schryver et al. (2006): Do dictionary users (frequently) look up words that are frequent in a corpus. Contrary to their results, our results that are based on the analysis of log files from two different online dictionaries indicate that users indeed look up frequent words frequently. When combining frequency information from the Mannheim German Reference Corpus and information about the number of visits in the Digital Dictionary of the German Language as well as the German language edition of Wiktionary, a clear connection between corpus and look-up frequencies can be observed. In a follow-up study, we show that another important factor for the look-up frequency of a word is its temporal social relevance. To make this effect visible, we propose a de-trending method where we control both frequency effects and overall look-up trends.