Investigation of features for extraction of named entities from texts in Russian

Abstract

This paper considers various features for extracting named entities from texts in Russian, which are used within the approaches based on machine learning, including the features of a token itself (lexeme), as well as vocabulary, contextual, cluster, and two-stage features. The contribution of each feature to improving the quality of extraction of named entities is studied. The CRF-classifier is used as a method of machine learning in the experiments that are described in this paper. The contribution of features is compared based on two open collections using the F-measure.