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This study aims to establish what lexical factors make it more likely for dictionary users to consult specific articles in a dictionary using the English Wiktionary log files, which include records of user visits over the course of 6 years. Recent findings suggest that lexical frequency is a significant factor predicting look-up behavior, with the more frequent words being more likely to be consulted. Three further lexical factors are brought into focus: (1) age of acquisition; (2) lexical prevalence; and (3) degree of polysemy operationalized as the number of dictionary senses. Age of acquisition and lexical prevalence data were obtained from recent published studies and linked to the list of visited Wiktionary lemmas, whereas polysemy status was derived from Wiktionary entries themselves. Regression modeling confirms the significance of corpus frequency in explaining user interest in looking up words in the dictionary. However, the remaining three factors also make a contribution whose nature is discussed and interpreted. Knowing what makes dictionary users look up words is both theoretically interesting and practically useful to lexicographers, telling them which lexical items should be prioritized in lexicographic work.
This contribution summarizes the lessons learned from the organization of a joint conference on text analytics research by the Business, Economic, and Related Data (BERD@NFDI) and Text+ consortia within the National Research Data Infrastructure (NFDI) in Germany. The collaboration aimed to identify common ground and foster interdisciplinary dialogue between scholars in the humanities and in the business domain. The lessons learned include the importance of presenting research questions using textual data to establish common ground, similarities in methodology for processing textual data between the consortia, similarities in research data management, and the need for regular interconsortial discussions on textual analysis methods and data. The collaboration proved valuable for interdisciplinary dialogue within the NFDI, and further collaboration between the consortia is planned.
This chapter will present lessons learned from CLARIN-D, the German CLARIN national consortium. Members of the CLARIN-D communities and of the CLARIN-D consortium have been engaged in innovative, data-driven, and community-based research, using language resources and tools in the humanities and neigh-bouring disciplines. We will present different use cases and users’ stories that demonstrate the innovative research potential of large digital corpora and lexical resources for the study of language change and variation, for language documentation, for literary studies, and for the social sciences. We will emphasize the added value of making language resources and tools available in the CLARIN distributed research infrastructure and will discuss legal and ethical issues that need to be addressed in the use of such an infrastructure. Innovative technical solutions for accessing digital materials still under copyright and for data mining such materials will be presented. We will outline the need for close interaction with communities of interest in the areas of curriculum development, data management, and training the next generation of digital humanities scholars. The importance of community-supported standards for encoding language resources and the practice of community-based quality control for digital research data will be presented as a crucial step toward the provisioning of high quality research data. The chapter will conclude with a discussion of impor-tant directions for innovative research and for supporting infrastructure development over the next decade and beyond.