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Applying Probabilistic Tagging To Russian Poetry

Author

Listed:
  • Alexey Starchenko

    (National Research University Higher School of Economics)

  • Lev Kazakevich

    (National Research University Higher School of Economics)

  • Olga Lyashevskaya

    (National Research University Higher School of Economics)

Abstract

The poetic texts pose a challenge to full morphological tagging and lemmatization since the authors seek to extend the vocabulary, employ morphologically and semantically deficient forms, go beyond standard syntactic templates, use non-projective constructions and non-standard word order, among other techniques of the creative language game. In this paper we evaluate a number of probabilistic taggers based on decision trees, CRF and neural network algorithms as well as one state-of-the-art dictionary-based tagger. The taggers were trained on prosaic texts and tested on three poetic samples of different complexity. Firstly, we discuss the method to compile the gold standard datasets for the Russian poetry. Secondly, we focus on the taggers’ performance in the identification of the part of speech tags and lemmas. These two annotation layers are key to compiling the corpus-based dictionaries, which we consider a long-term goal of our project

Suggested Citation

  • Alexey Starchenko & Lev Kazakevich & Olga Lyashevskaya, 2018. "Applying Probabilistic Tagging To Russian Poetry," HSE Working papers WP BRP 76/LNG/2018, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:76/lng/2018
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    More about this item

    Keywords

    natural language processing; full morphology tagging; NLP evaluation; Russian language; Russian poetry;
    All these keywords.

    JEL classification:

    • Z - Other Special Topics

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