Корпусные инструменты в грамматических исследованиях русского языка
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.