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Standards in CLARIN
(2022)
This chapter looks at a fragment of the ongoing work of the CLARIN Standards Committee (CSC) on producing a shared set of recommendations on standards, formats, and related best practices supported by the CLARIN infrastructure and its participating centres. What might at first glance seem to be a straightforward goal has over the years proven to be rather complex, reflecting the robustness and heterogeneity of the emerging distributed digital research infrastructure and the various disciplines and research traditions of the language-based humanities that it serves and represents, and therefore part of the chapter reviews the various initiatives and proposals that strove to produce helpful standards-related guidance. The focus turns next to a subtask initiated in late 2019, its scope narrowed to one of the core activities and responsibilities of CLARIN backbone centres, namely the provision of data deposition services. Centres are obligated to publish their recom-mendations concerning the repertoire of data formats that are best suited for their research profiles. We look at how this requirement has been met by the particular centres and suggest that having centres maintain their information in the Standards Information System (SIS) is the way to improve on the current state of affairs.
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
(2009)
In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.