Иноязычные заимствования в лексиконе участников фондового рынка второй половины XIX – начала XXI вв.
The author considers the evolution of the foreign language borrowings in the official and slang stock market participants' russian lexis since the IInd half of the XIXth century till the beginning of the XXIst century. The borrowed lexical units destiny during the Soviet and modern periods is considered.
The supplement to the thesis on Russian stock and currency markets paricipants' lexis formation, content and functionning represents the collected lexical material.
A lot of dictionaries of youth jargon (traditionally called youth slang) were published in Germany over the period from 2000 to 2013. They fall into three categories. The first group are annual editions of multilingual dictionaries by PONS and Langenscheidt publishers which give words and collocations used by schoolchildren from Germany, Austria and Switzerland their English, French and Spanish equivalents. These dictionaries, containing from 300 to 1500 words, focus on new entries and generally lack lexicographical information. It is native speakers, who contribute to the new editions by sending words and collocations to editors. These terms are included without any restrictions and this fact is mentioned in the preface to the dictionary. The second group are dictionaries ("Wörterbuch der Szenesprachen" and "Das neue Wörterbuch der Szenesprachen" from Duden publisher edited by P. Vipperman) organized by topic. They contain words referring to music, popular culture, love and sex, computers and Internet, entertainment and fashion etc. Each topic provides a specific vocabulary used by youth groups who share the same interests and have a similar lifestyle. These lexicographical sources feature thorough word definitions with detailed interpretation of jargonisms, normally in the context, but they do not give any information on grammar and language style, histories of words or verifiable reference. There is usually a website for a topical dictionary and anyone can participate in compiling it. The third group are lexicons by G. Eman who put them together after many years of close contact with young people of different age groups and social classes. Their strong points are complete definitions of entries. He also specifies stylistic register, polysemy, dialect, etymology, though sometimes his explanations lack system. All words and expressions are given in the context. G. Eman also provides synonymous sequences, arranged by topic which could be found in the appendix to "Endgale" dictionary. None of the reviewed dictionaries meets all standards for lexicographical reference. However, despite this shortcoming and diverse lexicographical aspects, dictionaries of German youth language published in the early 21st century are a valuable source for studying this layer of unconventional vocabulary and therefore youth subculture. It should be pointed out that German youth slang is not about specific words and expressions only, it reflects social relations, way of life and culture of young people from Germany, Austria and Switzerland.
The article compares the slang used by German students in the XIXth century and the slang used by German youth in the XXIth century. The research findings are similarities in lexico-thematic groups and also common tendencies in the way the vocabulary is enriched (metaphors, borrowing and world-building). Every case is proved with examples from dictionaries published in different centuries.
The author shows the productive derivation and new lexical units formation methods in stock and currency market participants slang by the example of the lexico-thematic group "Stock market goods, their lists, properties, exchange documents and rules".
This study discusses a number of methods that can be used jointly for error detection and correction, namely blacklists and pre-compiled dictionaries, a word2vec model, an N-gram language model and a tripartite error model. Our system consists of two standalone modules, an error detection confidence classifier, built with the help of supervised machine learning methods, and a corrector that processes words flagged as misspellings by the classifier. The error detection classifier uses word2vec filtered vector scores as one of the features. Apart from that, to achieve higher accuracy while having little training data, we use a hybrid error model that combines three approaches: the traditional channel model that uses single letter edits, the model introduced by Brill and Moore, and an extended version of the channel model that uses wider context edits. Combining these tools and methods we achieved rather promising results: our system effectively handles both known and unknown words, including difficult cases such as slang.
By the example of the lexico-semantic group 'Stock market goods, their lists, properties, stock exchange documents and rules' the author shows the origin of traders' slang words and expressions and the structural, derivational and lexicological aspects of stock-market participants' lexis system relations.