An Exploratory Study on Sociolinguistic Variation of Russian Everyday Speech

Bogdanova-Beglarian, Natalia; Sherstinova, Tatiana Y.; Blinova, Olga; Martynenko, Gregory Y.

doi:10.1007/978-3-319-43958-7_11

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…These corpora contain comments from the social networks Odnoklassniki, Pikabu and the Dvach forum. Studies in sociolinguistics often use corpora of oral communication (Sherstinova, 2009;Bogdanova-Beglarian et al, 2016;Cui, 2019). The most representative corpus of oral communication in Russian is the ORD ("One Speaker's Day"), which contains 240 hours of recordings of everyday telephone conversations (Asinovsky et al, 2009;Sherstinova, 2009).…”

Section: Politeness and Multimedia Corporamentioning

confidence: 99%

Towards a Russian Multimedia Politeness Corpus

Klokova,

Krongauz,

Shulginov

et al. 2023

Computational Linguistics and Intellectual Technologies”

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Communication involves an exchange of information as well as the use of linguistic means to begin, sustain, and end conversations. Politeness is seen as one of the major language tools that facilitate smooth communication. In English, politeness has been an area of great interest in pragmatics, with various theories and corpus annotation approaches used to understand the relationship between politeness and social categories like power and gender, and to build Natural Language Processing applications. In Russian linguistics, politeness research has largely focused on lexical markers and speech strategies. This paper introduces the ongoing work on the development of the Russian Multimedia Politeness Corpus and discusses an annotation framework for oral communicative interaction, with an emphasis on adapting politeness theories for discourse annotation. The proposed approach lies in the identification of frames that encompass contextual information and the selection of relevant spatial, social, and relational features for the markup. The frames are then used to describe standard situations, which are marked by typical intentions and politeness formulae and paraverbal markers.

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Section: Politeness and Multimedia Corporamentioning

confidence: 99%

Towards a Russian Multimedia Politeness Corpus

Klokova,

Krongauz,

Shulginov

et al. 2023

Computational Linguistics and Intellectual Technologies”

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“…In storytelling, writers often have their own styles rooted in different syntactic tendencies (Feng et al, 2012), allowing syntax to become indicators in prediction tasks such as gender (Sarawgi et al, 2011) and authorship (Raghavan et al, 2010) attribution. Syntax also has social connotations in different cultures -for example, in Russia, different social and demographic groups tend to use different syntactic patterns (Bogdanova-Beglarian et al, 2016). Such examples makes syntactic consistency crucial to capture in tasks such as machine translation and dialogue generation so that social conventions are not lost.…”

Section: Introductionmentioning

confidence: 99%

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

Chen,

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance-and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance-and document-level syntactic similarity which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels. FastKAS-SIM is more robust to syntactic dissimilarities and runs up to to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus. FastKASSIM's improvements allow us to examine hypotheses in two settings with large documents. We find that syntactically similar arguments on r/ChangeMyView tend to be more persuasive, and that syntax is predictive of authorship attribution in the Australian High Court Judgment corpus. * denotes equal contribution.Utterance 1: When we hate, we always move away from the grace of God. When we become resentful and unforgiving, the world around us seems spiteful and meaningless.Utterance 2: How can you be skiing if you are already swimming?FastKASSIM Score: 0.

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“…In storytelling, writers often have their own styles rooted in different syntactic tendencies (Feng et al, 2012), allowing syntax to become indicators in prediction tasks such as gender (Sarawgi et al, 2011) and authorship (Raghavan et al, 2010). Syntax also has social connotations in different cultures -for example, in Russia, different social and demographic groups tend to use different syntactic patterns (Bogdanova-Beglarian et al, 2016). Such examples makes syntactic consistency crucial to capture in tasks such as machine translation and dialogue generation.…”

Section: Introductionmentioning

confidence: 99%

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

Chen¹,

Chen²,

Zhou³

2022

Preprint

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Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance-and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance-and document-level syntactic similarity which pairs and averages the most similar dependency parse trees between a pair of documents based on tree kernels. FastKAS-SIM is more robust to syntactic dissimilarities and differences in length, and runs up to to 5.2 times faster than our baseline method over the documents in the r/ChangeMyView corpus. * denotes equal contribution.Utterance 1: When we hate, we always move away from the grace of God. When we become resentful and unforgiving, the world around us seems spiteful and meaningless. Utterance 2: How can you be skiing if you are already swimming? FastKASSIM Score: 0.219 CASSIM Score: 0.838 LSM Score: 0.623 Utterance 1: I like swimming because it is cool. Utterance 2: I love running because it is fun.

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An Exploratory Study on Sociolinguistic Variation of Russian Everyday Speech

Cited by 12 publications

References 4 publications

Towards a Russian Multimedia Politeness Corpus

Towards a Russian Multimedia Politeness Corpus

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

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