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Prosodic Boundaries Prediction in Russian Using Morphological and Syntactic Features

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Artificial Intelligence and Natural Language (AINL 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1119))

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Abstract

The paper presents a comparison between three approaches towards prosodic boundary prediction in Russian text, namely a rule-governed method and methods involving statistical classifier and deep learning technique. The methods aim to predict all possible prosodic boundaries in text applying morphological and syntactic information. All used features were described in terms of Universal Dependencies framework by means of SyntaxNet parser. The rule-governed method runs in a bottom-up fashion, using the information about syntax group edges and applying data-driven and hand-written linguistic rules. For machine learning methods, conditional random fields classifier and bidirectional LSTM model were built, with such features as part-of-speech tag, syntactic dependency type, syntactic relation embedding and presence of syntactic link between the current and adjacent words. As experimental material, we used the data of CORPRES corpus, containing over 30 hours of professionally read speech. Used separately, morphological features are slightly superior to syntactic ones, and their combination improves the results. BiLSTM yields the highest F1 measure value of 90.4, as compared to 88.8 for CRF and 83.1 for rule-based method.

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Authors and Affiliations

  1. Saint Petersburg State University, St. Petersburg, 199034, Russia

    Alla Menshikova & Daniil Kocharov

Authors
  1. Alla Menshikova
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  2. Daniil Kocharov
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Correspondence to Alla Menshikova .

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Editors and Affiliations

  1. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Dmitry Ustalov

  2. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  3. Computer Science, University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

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Menshikova, A., Kocharov, D. (2019). Prosodic Boundaries Prediction in Russian Using Morphological and Syntactic Features. In: Ustalov, D., Filchenkov, A., Pivovarova, L. (eds) Artificial Intelligence and Natural Language. AINL 2019. Communications in Computer and Information Science, vol 1119. Springer, Cham. https://doi.org/10.1007/978-3-030-34518-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-34518-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34517-4

  • Online ISBN: 978-3-030-34518-1

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