Визуализация данных для каталога русских лексических конструкций (на материале НКРЯ)
Our research aims at automatic identification of constructions associated with particular lexical items and its subsequent use in building the catalogue of Russian lexical constructions. The study is based on the data extracted from the Russian National Corpus (RNC, http://ruscorpora.ru). The main accent is made on extensive use of morphological and lexico-semantic data drawn from the multi-level corpus annotation. Lexical constructions are regarded as the most frequent combinations of a target word and corpus tags which regularly occur within a certain left and/or right context and mark a given meaning of a target word. We focus on nominal constructions with target lexemes that refer to speech acts, emotions, and instruments. The toolkit that processes corpus samples and learns up the constructions is described. We provide analysis for the structure and content of extracted constructions (e.g. r:ord der:num t:ord r:qual|pervyj ‘first’ + LJUBOV’ ‘love’; LJUBOV’ ‘love’ + PR|s ‘from’ + ANUM m sg gen|pervyj ‘first’ + S f inan sg gen|vzgljad ‘sight’ = love at first sight). As regards their structure, constructions may be considered as n-grams (n is 2 to 5). The representation of constructions is bipartite as they may combine either morphological and lemma tags or lexical-semantic and lemma tags. We discuss the use of visualization module PATTERN.GRAPH that represents the inner structure of extracted constructions.
The paper is intended to describe the experience of using the authentic linguistic corpus materials within the project "Creating an electronic textbook of Russian as a foreign language". Special attention is paid to the fundamental principles of the new project – automatic adaptation of RNC’s linguistic material. Worked out by means of information technologies, the product is supposed to adapt the complexity of authentic texts in terms of their syntactic and morphologic structures and vocabulary. The stages indispensable to attain the objective are also explained in the article. The paper describes not only the algorithm for solving the tasks and the final result of the research, but also the difficulties, which the developers face, and their solutions.
The paper discusses the standardization efforts to create a morphological standard for the Middle Russian corpus, which is part of the historical collection of the Russian National Corpus (RNC). To meet the needs of different categories of corpus researchers as well as NLP developers, we consider two styles of the morphological annotation (RNC schema and Universal Dependencies schema). A number of specifications of the feature list proposed to facilitate data reusability, linking and conversion.
The goal of the study is to show links between lexical and social diachronic change. The study is conducted in the culturomics framework (Michel et al 2011). In contrast to the Big data approach the study promotes the idea of medium data, i.e. amount of data which allows both to make quantitative and qualitative analysis.The research is based on the data from Russian National Corpus (ruscorpora.ru). The study pursues changes of context frequencies for the lexeme road in the period from 1800 till 2000, and correlates the observations with social and economic progress as well as change in conceptual language space
A new electronic frequency dictionary shows the distribution of grammatical forms in the inflectional paradigm of Russian nouns, adjectives and verbs, i.e. the grammatical profile of individual lexemes and lexical groups. While the frequency hierarchy of grammatical categories (e.g. the frequency of part of speech classes or the average ratio of Nominative to Instrumental case forms) has long been the standard topic of research, the present project shifts the focus to the distribution of grammatical forms in particular lexical units. Of particular concern are words with certain biases in grammatical profile, e.g. verbs used mostly in Imperative, in past neutral or nouns used often in plural. The dictionary will be a source for many of the future research in the area of Russian grammar, paradigm structure, grammatical semantics, as well as variation of grammatical forms.
The resource is based on the data of the Russian National Corpus. The article addresses some general issues such as corpora use in compiling frequency resources and technology of corpus data processing. We suggest certain solutions related to the selection of data and the level of granularity of grammatical profile. Text creation time and language registers are discussed as parameters which may shape the grammatical profile fluctuations.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.
Corpus linguistics can be broadly defined in terms of two partially overlapping research dimensions . On the one hand, corpus linguistics is knowledge of how to compile and annotate linguistic corpora. On the other hand, corpus linguistics is a family of qualitative and quantitative methods of language study based on corpus data. The book presents the first steps taken by Russian corpus linguistics toward the development of language corpora and corpus-based resources as well as their use in grammatical and lexical analysis.
The first part of the book focuses on the annotation of Russian texts at several levels: lemmas, part of speech and inflectional forms, word formation, lexical-semantic classes, syntactic dependencies, semantic roles, frames, and lexical constructions. We discuss various theoretical principles and practical considerations motivating the corpus markup design, provide details on the creation of lexical resources (electronic dictionaries and databases) and text processing software, and consider complicated cases that present challenges for the annotation of corpora both manually and automatically. In most cases we describe the annotation of the Russian National Corpus (RNC, ruscorpora.ru) and its affiliate project FrameBank (framebank.ru).
Frequency data depend not only on the representativeness and balance of texts in a corpus, but also on the rules and tools used for annotation. The book addresses the development of evaluation standards for Russian NLP resources, namely, morphological taggers and dependency parsers. In addition, the book presents several experiments on automatic annotation and disambiguation: lemmatization of word forms not in the dic- tionary; word sense disambiguation based on vectors formed by lexical, semantic and grammatical cues of context; and semantic role labeling.
The final chapters of the first part of the book outline two types of frequency dictionaries based on the RNC data: a general-purpose frequency dictionary and a lexico-grammatical one.
The second part of the book presents an analysis of corpus data and includes a number of case studies of Russian grammar and lexical-grammatical interaction using quantitative methods. The key concept underlying our analysis is the behavioral profile (Hanks 1996; Divjak, Gries 2006), which is the frequency distribution of variable elements in a linguistic unit as attested in a corpus. This covers grammatical profiles (the frequency distribution of inflected forms of a word), constructional profiles (the frequency distri- bution of argument or any other constructions attested for a key predicate), lexical and semantic profiles (the frequency distribution of words and lexical-semantic classes in construction slots or, more generally, in the context of a word), and radial category profiles (the frequency distribution of word senses and word uses across the radial category network of a polysemous unit). We use grammatical, constructional, semantic, and radial category profiling to study tense, aspect and mood specialization of Russian verb forms; to identify singular-oriented and plural-oriented nouns; to investigate factors for prefix choice and prefix variation in natural perfectives (chistovidovye perfectivy); to analyze constraints on the filling of slots in a construction and how this affects the meaning of the construction, taking as an example the Genitive construction of shape and the spatial construction with the preposition poverkh ‘up and over’.
The quantitative corpus-based techniques used for the analysis vary from simple descriptive statistics (e. g., absolute frequencies, percentages, measures of the central ten- dency and outliers) to exact Fisher test and logistic regression. We claim that the vector modeling approaches to quantitative grammatical studies in theoretical linguistics are no less effective than in computational linguistics, where they have become a standard tool.
Philological research, especially in the field of literature, is usually considered a "thing-in-itself"; the intrinsic value of this phenomenon involves extremely intuitive, creative, "human-readable" analysis. Meanwhile, modern variety of computer programs (semantic text referentors, tag clouds, concordansers, etc.), created also for the humanities, such as sociology, psychology, management, cannot but draw a philologist’s attention. The steps, how to work with a parallel subcorpus in Russian National Korpus, described in detail. Reviewed freeware LR aligner (for non-commercial use), compares translations in Russian the novel "All Red" by J.Chmielewska. As examples of lexical items selected the modal word "avos’", the word "nakonets" as an introductory and the circumstances of the word "ves’" and "tsely". The Program LF aligner treated three translations of the novel, the authors are M.Krongauz, V.Selivanova, O. Kuznetsova. Consistent description of the existing programs, testing them on art material and comparison of the received data with the existing traditional research, especially in the field of philology and foreign language teaching, is a new step of a text analysis.