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Digressions
(2015)
Der Beitrag von Bruno Strecker Digressions ist auf Französisch geschrieben (der Muttersprache von Jacqueline Kubczak) und handelt von unterschiedlichen Exkursen. Er macht die Verbindung zwischen Kommunikationssituation und Arten der Exkurse sichtbar und bietet eine darauf basierende Typologie der Exkurse an. In einem zweiten Schritt werden die formalen Möglichkeiten, einen Exkurs einzuleiten und zu formulieren, dargestellt (z. B. durch Appositionen, Parenthesen, festgelegte Ausdrucksformen wie A propos xxx, Ça me rappelle oder nicht eingebettete Phrasen). Schließlich zeigt er, wie man aus dem Exkurs wieder „in die Spur“ kommt.
We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use—both individually and collectively—over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.