Abstract:This study aims to test two principles of code-switching (CS) formulated by González Vilbazo (2005): The Principle of the Functional Restriction (PFR) and the Principle of Agreement (PA). The first states that a code-switch between the morphological exponents of functional heads belonging to the same extended projection of a lexical category (N • or V • ) is not possible. The second claims that inside a phrase, agreement requirements have to be satisfied, regardless of the language providing the lexical material. The corpus on which we tested these hypotheses consists of 25,947 authentic text messages collected in Switzerland in 2009 and 2010. In our corpus, the PA is maintained. The PFR also seems to hold, even if data is limited. Interestingly, contradicting examples can be explained by phonological principles or the sociolinguistic background of the authors, who are not native speakers. Overall, the evidence found in spontaneously written non-standard data like text messages seems to confirm the validity of the two principles.
In this paper, we present evidence in favour of a syntactic approach to subject drop in Swiss French text messages. Subject drop in our corpus follows patterns found in various so-called "written abbreviated registers" such as diaries, notes etc.: it occurs at the beginning of main sentences and after preposed adjuncts. Based on a corpus of 1100 text messages, collected in 2009/10 (www.sms4science.ch), we test predictions put forward by two approaches to argument drop in abbreviated registers, i.e. the "Avoid Weak Start" hypothesis by Weir (2012a) and the "Truncated CP hypothesis" by Haegeman (2013). While for our data the first approach cannot be excluded, our results more strongly support the syntactic one, despite the fact that some data, especially preposed strong subject moi without clitic resumption, challenge existing analyses. These data suggest that dropped referential subjects can be analysed as instances of familiar topic drop.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.