Why we need a gradient approach to word order

This paper argues for a gradient approach to word order, which treats word order preferences, both within and across languages, as a continuous variable. Word order variability should be regarded as a basic assumption, rather than as something exceptional. Although this approach follows naturally from the emergentist usage-based view of language, we argue that it can be beneficial for all frameworks and linguistic domains, including language acquisition, processing, typology, language contact, language evolution and change, variationist linguistics and formal approaches. Gradient approaches have been very fruitful in some domains, such as language processing, but their potential is not fully realized yet. This may be due to practical reasons. We discuss the most pressing methodological challenges in corpus-based and experimental research of word order and propose some solutions and best practices.

THE POTENTIAL OF GOOGLE SEARCH FOR STUDIES IN COGNITIVE CORPUS LINGUISTICS

The paper investigates the possibility to employ the Google search system as an analogue of the corpus of texts for potential use in further cognitive research of a language. The purpose of the article is to elucidate the significance of statistical data available due to the use of different operators and filters of the search system in the study of cognitive mechanisms of representation of linguistic reality in speech. Experimental observations have been made to compare the results of competing queries in Google and Russian National Corpus based on the word order variability of binomial phrases. The results obtained showed that the volume and variety of language data justifies the employment of Google for these purposes. This leads to the conclusion that these data can be considered valid as linguistic material for further interpretation in cognitive language research

Studying bright variable stars with the Multi-site All-Sky CAmeRA (MASCARA)

Context. The Multi-site All-Sky CAmeRA (MASCARA) aims to find the brightest transiting planet systems by monitoring the full sky at magnitudes 4 < V < 8.4, taking data every 6.4 s. The northern station has been operational on La Palma since February 2015. These data can also be used for other scientific purposes, such as the study of variable stars. Aims. In this paper we aim to assess the value of MASCARA data for studying variable stars by determining to what extent known variable stars can be recovered and characterised, and how well new, unknown variables can be discovered. Methods. We used the first 14 months of MASCARA data, consisting of the light curves of 53 401 stars with up to one million flux points per object. All stars were cross-matched with the VSX catalogue to identify known variables. The MASCARA light curves were searched for periodic flux variability using generalised Lomb–Scargle periodograms. If significant variability of a known variable was detected, the found period and amplitude were compared with those listed in the VSX database. If no previous record of variability was found, the data were phase folded to attempt a classification. Results. Of the 1919 known variable stars in the MASCARA sample with periods 0.1 < P < 10 days, amplitudes >2%, and that have more than 80 h of data, 93.5% are recovered. In addition, the periods of 210 stars without a previous VSX record were determined, and 282 candidate variable stars were newly identified. We also investigated whether second order variability effects could be identified. The O’Connell effect is seen in seven eclipsing binaries, of which two have no previous record of this effect. Conclusions. MASCARA data are very well suited to study known variable stars. They also serve as a powerful means to find new variables among the brightest stars in the sky. Follow-up is required to ensure that the observed variability does not originate from faint background objects.