Параллельные белорусско-русский и русско-белорусский корпусы: совместный проект Национального корпуса русского языка
The article discusses the most recent trends in the development of the English progressive. A corpus-based approach to linguistic research is seen as an effective means of determining reliability of the data retrieved and helps track the major diachronic dynamic in the increasing frequency of the progressive aspect that has taken place since the beginning of the 20th century. The article specifically deals with the extension of the progressive to new constructions, such as modal, present perfect and past perfect passive progressive, and also accounts for the use of progressive forms in the contextual environment not generally characteristic of them.
This paper is devoted to the use of two tools for creating morphologically annotated linguistic corpora: UniParser and the EANC platform. The EANC platform is the database and search framework originally developed for the Eastern Armenian National Corpus (www.eanc.net) and later adopted for other languages. UniParser is an automated morphological analysis tool developed specifically for creating corpora of languages with relatively small numbers of native speakers for which the development of parsers from scratch is not feasible. It has been designed for use with the EANC platform and generates XML output in the EANC format.
UniParser and the EANC platform have already been used for the creation of the corpora of several languages: Albanian, Kalmyk, Lezgian, Ossetic, of which the Ossetic corpus is the largest (5 million tokens, 10 million planned for 2013), and are currently being employed in construction of the corpora of Buryat and Modern Greek languages. This paper will describe the general architecture of the EANC platform and UniParser, providing the Ossetic corpus as an example of the advantages and disadvantages of the described approach.
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.
Four electronic corpora created in 2011 within the framework of the “Corpus Linguistics: the Albanian, Kalmyk, Lezgian, and Ossetic Languages” Program of Fundamental Research of the RAS are presented. The interface and functionalities of these corpora are described, engineering problems to be solved in their creation are elucidated, and the promises of their development are discussed. A particular emphasis is made on the compilation of dictionaries and automatic grammatical markup of the corpora.
The paper presents a project aimed at the development of a Russian Learner Parallel Corpus, discusses the existing analogues, describes the current status and the tasks in which it could be used. The existing parallel corpora contain (comparatively) “correct” translations; whereas the aim of the present project is to create a sufficiently large corpus of imperfectly translated Russian and English texts together with their sources and use it as a tool for translation studies, especially those related to translation mistakes. The new corpus will be a valuable resource for computational linguistics as it provides another way of getting data for evaluation which could be used to improve machine translation systems. As of now, the corpus is available on-line, it already contains nearly half a million word tokens and is growing. The main source of material is translations made by student translators in Russian universities.