Экскламативы в русском языке: Корпусное исследование
This is an interdisciplinary volume that focuses on the central topic of the representation of events, namely cross-cultural differences in representing time and space, as well as various aspects of the conceptualisation of space and time. It brings together research on space and time from a variety of angles, both theoretical and methodological. Crossing boundaries between and among disciplines such as linguistics, psychology, philosophy, or anthropology forms a creative platform in a bold attempt to reveal the complex interaction of language, culture, and cognition in the context of human communication and interaction.
The authors address the nature of spatial and temporal constructs from a number of perspectives, such as cultural specificity in determining time intervals in an Amazonian culture, distinct temporalities in a specific Mongolian hunter community, Russian-specific conceptualisation of temporal relations, Seri and Yucatec frames of spatial reference, memory of events in space and time, and metaphorical meaning stemming from perception and spatial artefacts, to name but a few themes.
The article discusses one argument in favor of descriptive theory of reference of proper names against the theory of direct reference which appeals to a famous example of the ship of Theseus. The author defends the latter theory by means of distinguishing the object of direct reference and its principles of individuation. The argument is discussed with reference to the works of H. Chandler, L. Linsky, S. Kripke, N. Salmon and other theorists.
The project we present – Russian Learner Translator Corpus (RusLTC) is a multiple learner translator corpus which stores Russian students’ translations out of English and into it. The project is being developed by a cross-functional team of translator trainers and computational linguists in Russia. Translations are collected from several Russian universities; all translations are made as part of routine and exam assignments or as submissions for translation contests by students majoring in translation. As of March 2014 RusLTC contains the total of nearly 1.2 million word tokens, 258 source texts, and 1,795 translations. The paper gives a brief overview of the related research, describes the corpus structure and corpus-building technologies used; it also covers the query tool features and our error annotation solutions. In the final part we make a summary of the RusLTC-based research, its current practical applications and suggest research prospects and possibilities.
This book is a collection of articles dealing with various aspects of grammatical relations and argument structure in the languages of Europe and North and Central Asia (LENCA). Topics covered with respect to individual languages are: split-intransitivity (Basque), causativization (Agul), transitives and causatives (Korean and Japanese), aspectual domain and quantification (Finnish and Udmurt), head-marking principles (Athabaskan languages), and pragmatics (Eastern Khanty and Xibe). Typology of argument-structure properties of ‘give’ (LENCA), typology of agreement systems, asymmetry in argument structure, typology of the Amdo Sprachbund, spatial realtors (Northeastern Turkic), core argument patterns (languages of Northern California), and typology of grammatical relations (LENCA) are the topics of articles based on cross-linguistic data. The broad empirical sweep and the fine-tuned theoretical analysis highlight the central role of argument structure and grammatical relations with respect to a plethora of linguistic phenomena.
In this article we present the results of research into discourse features characterising a lexico-semantic group of synonyms denoting a human being: human being, person, individual, personality and man. The main tool for analysis was language corpora, which made it possible not only to determine more precisely the functional styles the lexemes tend to be used in, but also to describe thematic characteristics of the texts in which the analysed lexical units show the highest frequency of use