论某些流行文学体裁中的动词结构频率

N. N. Buylova, O. Lyashevskaya
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引用次数: 0

摘要

基于语料库的语言研究是现代语言学的一种广泛实践。在我们的研究中,我们通过文本语料库来处理所谓的大众文学(或准文学)。大众文学的规范化使我们可以用文学公式来描述它的体裁。简而言之,一个公式用于以普遍形式体现文化主题和刻板印象。虽然大众文学是文学和文化研究的共同主题,但从语言学的角度来看,文学公式的研究还不够好。我们认为,副文学微体裁之间的差异可能不仅存在于词汇上,还存在于句法上。我们的工作以大众文学语料库为基础,分析了微体裁(爱情小说、侦探小说、科幻小说和奇幻小说)的动词结构特征。为了识别大众文学微体裁的鲜明特征,我们进行了一系列的机器学习实验。作为一个数据集,我们编译了一个属于四个微体裁的1200个文本的语料库。在我们之前的研究中,我们表明统计特征(文本长度、句子长度和词频)不足以成功分类。词汇特征的使用提高了机器学习的质量,而基于句法特征的分类器表现出最好的效果。81个结构被选为基于句法的机器学习最重要的特征。动词结构由“动词+依赖关系”组成。我们考虑了几种类型的依赖关系:论点(主语,直接和间接宾语)和形容词(从句和状语分类器)。这些结构是根据动词配价的丰满度来描述的。有几种类型的结构是有区别的:完全的、不完全的(省略直接或间接的补语)和扩展的(带补语)。分析和描述了每种微体裁中动词结构的具体例子。从结构轮廓的相似性来看,言情小说和侦探小说以动词和直接引语结构为主,而科幻小说和奇幻小说则以完整结构为主。对我们的观察也提供了可能的非文本解释。研究中提出的方法可用于研究各种句法特征,例如与作者、时间或体裁特异性相关的句法特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Verbal Construction Frequency in Certain Genres of Popular Literature
Corpora-based language studies is a widespread practice in modern linguistics. In our study, we address so-called mass literature (or paraliterature) via a corpus of texts. The standardization of mass literature allows us to describe its genres by applying literary formulas. In brief, a formula serves for the embodiment of cultural themes and stereotypes in a universal form. While mass literature is a common subject of literary and cultural studies, from a linguistic point of view, literary formulas have not been studied well enough. We suggest that differences between the microgenres of paraliterature may be in syntax as well as in the vocabulary. Our work is based on the mass literature corpora and provides analysis of verb constructions characteristic of microgenres (love story, detective story, science fiction novel, and fantasy). In order to identify the distinctive features of mass literature microgenres, we have conducted a series of machine learning experiments. As a dataset, we compiled a corpus of 1,200 texts belonging to four microgenres. In our previous studies we showed that statistical features (text length, sentence length, and parts of speech frequencies) were insufficient for successful classification. The usage of lexical features has improved the quality of machine learning, however, the classifier based on syntactic features has shown the best results. 81 constructions have been selected as the most important features of syntactic-based machine learning. A verb construction consists of a “verb + dependencies”. We consider several types of dependencies: arguments (subject, direct and indirect objects) and adjuncts (subordinate clause and adverbial classifier). The constructions are described in terms of the fullness of the verb valencies. Several types of constructions are distinguished: complete, incomplete (with omissions of direct or indirect complements), and extended (with adjuncts). Specific examples of verb constructions in each of the microgenres have been analyzed and described. Based on the similarity of the construction profile, romance novels and detective stories show the predominance of verbs and direct speech constructions, whereas science fiction novels and fantasy demonstrate the prevalence of full constructions. Possible non-textual explanations for our observations are also provided. The method proposed in the study can be used for the investigation of various kinds of syntactic features, for instance, those associated with the author’s, temporal or genre specificities.
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