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This article describes a series of ongoing efforts at the Stanford Literary Lab to manage a large collection of literary corpora (~40 billion words). This work is marked by a tension between two competing requirements – the corpora need to be merged together into higher-order collections that can be analyzed as units; but, at the same time, it’s also necessary to preserve granular access to the original metadata and relational organization of each individual corpus. We describe a set of data management practices that try to accommodate both of these requirements – Apache Spark is used to index data as Parquet tables on an HPC cluster at Stanford. Crucially, the approach distinguishes between what we call “canonical” and “combined” corpora, a variation on the well-established notion of a “virtual corpus” (Kupietz et al., 2014; Jakubíek et al., 2014; van Uytvanck, 2010).
Historical cabinet protocols are a useful resource which enable historians to identify the opinions expressed by politicians on different subjects and at different points of time. While cabinet protocols are often available in digitized form, so far the only method to access their information content is by keyword-based search, which often returns sub-optimal results. We present a method for enriching German cabinet protocols with information about the originators of statements. This requires automatic speaker attribution. In order to avoid costly manual annotation of training data, we design a rule-based system which exploits morpho-syntactic cues. Unlike many other approaches, our method can also deal with cases in which the speaker is not explicitly identified in the sentence itself. This is an important capability as 45% of all sentences in the data constitute reported speech whose speakers are not explicitly marked. Our system is able to detect implicit speakers by taking into account signals of speaker continuity. We show that such a system obtains good results, especially with respect to recall which is particularly important for information access.
We present a novel NLP resource for the explanation of linguistic phenomena, built and evaluated exploring very large annotated language corpora. For the compilation, we use the German Reference Corpus (DeReKo) with more than 5 billion word forms, which is the largest linguistic resource worldwide for the study of contemporary written German. The result is a comprehensive database of German genitive formations, enriched with a broad range of intra- und extralinguistic metadata. It can be used for the notoriously controversial classification and prediction of genitive endings (short endings, long endings, zero-marker). We also evaluate the main factors influencing the use of specific endings. To get a general idea about a factor’s influences and its side effects, we calculate chi-square-tests and visualize the residuals with an association plot. The results are evaluated against a gold standard by implementing tree-based machine learning algorithms. For the statistical analysis, we applied the supervised LMT Logistic Model Trees algorithm, using the WEKA software. We intend to use this gold standard to evaluate GenitivDB, as well as to explore methodologies for a predictive genitive model.