Глава
To automated generation of test questions on the basis of error annotations in EFL essays: a time-saving tool?
Данное пособие представляет собой двуязычный словарь для работы с научными и методическими текстами в предметной области лингводидактика.и может использоваться в образовательной среде педогагического вуза для поддержки поддержки самостоятельной работы студентов и исследовательской деятельности преподавателей.
Most modern software is written in high level languages. The task of translating source code, written in high-level languages, into a representation, which can be executed on a computer system, solves by specialized programs called compilers. Errors in compilers lead to differences between the behavior of modules, resulting from the work of compilers, and behavior, defining the semantics of the original program. Such errors are very difficult to detect and correct, and their presence casts doubt on the quality of the programs generated by a compiler. Obviously, the correctness of the compiler is a strong prerequisite for reliable software created with its help [20]. This paper describes the concept of a system designed to automate the process of testing the major components of any compiler: syntax analyzer and context conditions analyzer (semantic analyzer).
Пособие посвящено подготовке студентов 4 курса филологического факультетаРГРПУ им. А.И.Герцена к государственному экзамену по иностранному языку.
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.
Статья посвящена частотному типу ошибок, которые делают носители русского языка, а именно несовпадению падежа и предлога при эллипсисе в сочинительных конструкциях.
В статье описаны результаты реализации инструмента генерации тестовых данных, методом разбиения значений входных параметров системы на классы эквивалентности для автоматизации комплексного функционального тестирования информационных систем, управляемых XML-сообщениями.
пособие посящено разработке подготовительных материалов к государственному экзамену по английскому языку на филологическрм факультете РГПУ им. А.И. Герцена.
The paper describes the learner corpus composed of English essays written by native Russian speakers. REALEC (Russian Error-Annotated Learner English Corpus) is an error-annotated, available online corpus, now containing more than 200 thousand word tokens in almost 800 essays. It is one of the first Russian ESL corpora, dynamically developing and striving to improve both in size and in features offered to users. We describe our perspective on the corpus, data sources and tools used in compiling it. Elaborate self-made classification of learners’ errors types is thoroughly described. The paper also presents a pilot experiment on creating test sets for particular learners’ problems using corpus data.
В рамках данной статьи анализируются направления и условия использования результатов оценки качества образования, которые необходимо учитывать при планировании и проведении программы оценки учебных достижений школьников.