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Mit diesem Bild beschreibt Hermann Unterstöger in einem „Sprachlabor“- Artikel der Süddeutschen Zeitung vom 23.3.2013 die Erfolgsgeschichte, die das Substantiv (das) Narrativ in den letzten 30 Jahren vorgelegt hat. Während Unterstöger feinsinnig den intertextuellen Bezug zum „Narrenschiff“ des Sebastian Brant oder dem gleichnamigen Roman von Katherine Ann Porter bemüht, wird Matthias Heine, der Autor von „Seit wann hat geil nichts mehr mit Sex zu tun? 100 deutsche Wörter und ihre erstaunlichen Karrieren“ in einem Artikel in der WELT vom 13.11.2016, wie nach diesem Buchtitel zu erwarten, eher grob: Dort heißt es: „Hinz und Kunz schwafeln heutzutage vom ,Narrativ‘“.
In German there are about twenty-five elements (like gemäß, nahe, voll) that seem to be used as a preposition along with their use as an adjective. In former approaches the preposition is interpreted as the product of grammaticalizing (and/or reanalyzing) the adjective. It is argued that the two criteria these approaches rely on, namely change of linear position and change of case government, are insufficient. In this paper, seven criteria for distinguishing adjectives form prepositions in German are put forward. What is most important is that these criteria have to be evaluated on the token level as well as on the level of type and word class/syntactic category. It can be shown that the individual ‘adjective-prepositions' as types possess a specific mixture of adjective-like and preposition-like features. On the token level, occurring as part of a postnominal restrictive attribute is indicative for preposition-like status in German. The comparison of German with English and Italian adjective-prepositions (like near, far, due and vicino, lontano) reveals a lot of differences, which counts as evidence for the language-specific nature of word classes. Nevertheless, Lehmanns functional-typological approach uncovers a fundamental functional similarity between complement governing adjectives and prepositions: the primary function of the phrases, i.e., adjective/preposition + complement, is to modify a nominal or a verbal concept, respectively. This insight explains why adjective-prepositions can be found cross-linguistically. The question whether we should propose one type or two types for gemäß and its cognates is of minor importance only.
Emoticons erfreuen sich auf der ganzen Welt großer Beliebtheit, vor allem in der alltäglichen elektronischen Kommunikation wie E-Mail, SMS, Forumsdiskussionen, Instant Messaging, Facebook oder Twitter. Zum ersten Mal in der Geschichte wurde 2015 von den Sprachbeobachtern der britischen Oxford Dictionaries ein Emoticon zum Wort des Jahres gewählt: das Grinsegesicht, dem die Freudentränen aus den Augen spritzen (vgl.<www.sueddeutsche.de/kultur/britisches-wortdes-jahres-was-haben wir-gelacht-1.2740952>, Stand: 8.11.2017). Die Jury begründete ihre Wahl wie folgt: „[E]moji have come to embody a core aspect of living in a digital world that is visually driven, emotionally expressive, and obsessively immediate.“
Corpus researchers, along with many other disciplines in science are being put under continual pressure to show accountability and reproducibility in their work. This is unsurprisingly difficult when the researcher is faced with a wide array of methods and tools through which to do their work; simply tracking the operations done can be problematic, especially when toolchains are often configured by the developers, but left largely as a black box to the user. Here we present a scheme for encoding this ‘meta data’ inside the corpus files themselves in a structured data format, along with a proof-of-concept tool to record the operations performed on a file.
Am Anfang ist das Wort
(2017)
Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be filtered. This study briefly discusses the impact of web spam on corpus usability and emphasizes the importance of removing computer generated text from web corpora.
The paper also presents a keyword comparison of an unfiltered corpus with the same collection of texts cleaned by a supervised classifier trained using FastText. The classifier was able to recognize 71% of web spam documents similar to the training set but lacked both precision and recall when applied to short texts from another data set.