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This NECTAR track paper (NECTAR: new scientific and technical advances in research) summarizes recent research and curation activities at the CLARIN center Stuttgart. CLARIN is a European initiative to advance research in humanities and social sciences by providing language-based resources via a shared distributed infrastructure. We provide an overview of the resources (i.e., corpora, lexical resources, and tools) hosted at the IMS Stuttgart that are available through CLARIN and show how to access them. For illustration, we present two examples of the integration of various resources into Digital Humanities projects. We conclude with a brief outlook on the future challenges in the Digital Humanities.
Researchers in the (digital) humanities have identified a large potential in the use of automatic text analysis capabilities in their text studies, scaling up the amount of material that can be explored and allowing for new types of questions. To support the scholars’ specific research questions, it is important to embed the text analysis capabilities in an appropriate navigation and visualization framework. However, study-specific tailoring may make it hard to migrate analytical components across projects and compare results. To overcome this issue, we present in this paper the first version of TEANLIS (text analysis for literary scholars), a flexible framework designed to include text analysis capabilities for literary scholars.
A common way to express sentiment about some product is by comparing it to a different product. The anchor for the comparison is a comparative predicate like “better”. In this work we concentrate on the annotation of multiword predicates like “more powerful”. In the single-token-based approaches which are mostly used for the automatic detection of comparisons, one of the words has to be selected as the comparative predicate. In our first experiment, we investigate the influence of this decision on the classification performance of a machine learning system and show that annotating the modifier gives better results. In the annotation conventions adopted in standard datasets for sentiment analysis, the modified adjective is annotated as the aspect of the comparison. We discuss problems with this type of annotation and propose the introduction of an additional argument type which solves the problems. In our second experiment we show that there is only a small drop in performance when adding this new argument type.
