Combining Knowledge and CRF-Based Approach to Named Entity Recognition in Russian

  • V. A. MozharovaEmail author
  • N. V. LoukachevitchEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)


Current machine-learning approaches for information extraction often include features based on large volumes of knowledge in form of gazetteers, word clusters, etc. In this paper we consider a CRF-based approach for Russian named entity recognition based on multiple lexicons. We test our system on the open Russian collections “Persons-1000” and “Persons-1111” labeled with personal names. We additionally annotated the collection “Persons-1000” with names of organizations, media, locations, and geo-political entities and present the results of our experiments for one type of names (Persons) for comparison purposes, for three types (Persons, Organizations, and Locations), and five types of names. We also compare two types of labeling schemes for Russian: IO-scheme and BIO-scheme.


CRF Named entity recognition 



This work is partially supported by RFBR grant No. 15-07-09306.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lomonosov Moscow State UniversityMoscowRussia

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