Languages in Contrast - Volume 21, Issue 2, 2021

Hedging is a complex phenomenon with an indefinite number of potential realisations. The complexity and versatility of hedging strategies make them particularly interesting to study across languages. This contrastive study compares the realisations of the pragmatic function of hedging in everyday Norwegian and English conversations using data from four corpora of Norwegian and English informal spoken conversations (the Norwegian Speech Corpus, the Nordic Dialect Corpus, the BigBrother corpus, and the BNC2014). The results show that speakers of both languages mainly use pragmatic particles, adverbs, and first-/second-person pronouns + cognitive verbs [1/2 pers. + Cog. V] to express hedging. Furthermore, English speakers use significantly more [1/2 pers. + Cog. V] and modal verbs than Norwegian speakers, who use significantly more adjectives, prepositional phrases and clauses to hedge their utterances.

Based on the description of EU legislative varieties covering EU directives and their national laws of implementation in 11 languages, we are interested in the extent to which Eurolects are similar to each other, above and beyond trivial genealogical similarities. We thus utilise a variation-oriented aggregative analysis technique to address these questions: (a) What is the precise extent to which Eurolects are similar to each other? (b) Are similarities predicted by extra-linguistic affinities? (c) Do factors such as EU accession dates and language policy play a role in shaping the Eurolect clusters? Our methodology starts out from a meticulously catalogued list of corpus-based and corpus-driven lexical and grammatical features. Through the observed presence or absence of these features, we calculate in a second step the aggregate linguistic distances between all of the Eurolects. Finally, in step three, we use a well-established technique, Multidimensional Scaling, to visualize and interpret the Eurolect landscape.

N‑gram analysis (popularized e.g. by Biber et al., 1999) has become a popular method for the identification of recurrent language patterns. Although the extraction of n‑grams from a corpus may seem straightforward, it proves to be very challenging when applied cross-linguistically (cf. e.g. Ebeling and Ebeling, 2013; Granger and Lefer, 2013; Čermáková and Chlumská, 2017). The major issue is that the quantities of n‑grams of a certain length in typologically different languages do not correspond. Consequently, n‑grams of a given length may function differently across languages, rendering a direct comparison inadequate. Our paper introduces a function capable of modelling the relation between the quantities of n‑grams in typologically distant languages, using the example of Czech and English (and some other language pairs). Based on our model, we can suggest what n‑gram lengths should be contrasted to better reflect the size of n‑gram inventories in each language. The correspondence may not be intuitive (e.g. a Czech 2-gram may best correspond to an English 2.5-gram), but it still provides researchers with a general guide as to what might be useful to include in their analysis (e.g. in this case 2-grams in Czech and 2- and 3-grams in English).

This study aims to describe the melodic contours used in Spanish and English calling vocatives in order to identify cross-linguistic similarities and differences. Additionally, it also explores how the sociopragmatic factor of formality may condition contour choices in both languages. 18 speakers of Spanish and 18 speakers of English produced a total of 432 one-word vocatives in formal and informal situations. The analyses of the F0 contours revealed that, although Spanish and English share multiple melodies in this speech act (e.g. L+H* L%, L* H% and L+H* !H%), some tones are language-specific (e.g. L+H* HL% for Spanish). In addition, a General Linear Mixed Model confirmed that these contours are not equally attested in all contexts and that the formality of the situation can condition their use.

The aim of this paper is to explore and contrast the morphosyntactic and semantic features of two equivalent nonce echo constructions (NECs) expressing disapproval and annoyance (i.e. don’t (you) X me and ni X ni Y) in colloquial English and Spanish. A NEC is defined as a reactive clause containing duplicated words of the initiative, showing the property of nonceness, and having a communicative goal. Two types of NECs are found in both languages: an attitudinal echo construction and a referential one, the former being more idiomatic than the latter. Based on the premise that texts are necessarily dialogic, two sets of examples are obtained from English corpora (The Movie Corpus, The TV Corpus, and the Corpus of American Soap Operas); and from two Spanish corpora and a dataset (CREA, CORPES XXI, and the Dataset of Spanish Dialogic Texts Online). Findings suggest that both languages show negative and restricted syntactic templates. Variables are coinages that originate from the processes of conversion in English (e.g. don’t you John me) and gender polarity in Spanish (e.g. ni luna ni *luno). Hence, although both types of variables are morphologically novel and contextually meaningful, Spanish variables are generally ungrammatical and unlikely to exist outside the discursive frame under study.

This paper proposes to study the contrastive syntax of French and Chinese through the lens of syntactic mismatches, and by making use of parallel treebanks. A syntactic mismatch is the non-similarity between the syntactic structures of one linguistic unit and its translation. Syntactic mismatches are formalized using the notion of paraphrase from the Meaning-Text Theory, which allows for capturing mismatches at different levels of the linguistic description (e.g. Semantic, Deep-Syntactic, and Surface-Syntactic). In this paper, we report in details on the types of paraphrases found in the seed corpus used, demonstrating that the Deep-Syntactic paraphrases constitute the best starting point for our study. Then, we show how, starting from the seed corpus, we semi-automatically constructed a multi-layer parallel treebank with the alignment and annotation of paraphrases.