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Russian Tagging and Dependency Parsing Models for Stanford CoreNLP Natural Language Toolkit

  • Liubov KovriguinaEmail author
  • Ivan Shilin
  • Alexander Shipilo
  • Alina Putintseva
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)

Abstract

The paper concerns implementing maximum entropy tagging model and neural net dependency parser model for Russian language in Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. Russian belongs to morphologically rich languages and demands full morphological analysis including annotating input texts with POS tags, features and lemmas (unlike the case of case-, person-, etc. insensitive languages when stemming and POS-tagging give enough information about grammatical behavior of a word form). Rich morphology is accompanied by free word order in Russian which adds indeterminacy to head finding rules in parsing procedures. In the paper we describe training data, linguistic features used to learn the classifiers, training and evaluation of tagging and parsing models.

Keywords

Dependency parsing Neural net dependency parsing Dependency parsing for Russian language Russian models for Stanford CoreNLP Maxent tagger Label embeddings 

Notes

Acknowledgment

This work was financially supported by the Russian Fund of Basic Research (RFBR), Grant No. 16-36-60055.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Liubov Kovriguina
    • 1
    Email author
  • Ivan Shilin
    • 1
  • Alexander Shipilo
    • 1
    • 2
  • Alina Putintseva
    • 1
  1. 1.ITMO UniversitySaint-petersburgRussia
  2. 2.SPbSUSaint-petersburgRussia

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