Russian Named Entities Recognition and Classification Using Distributed Word and Phrase Representations

The paper presents results on Russian named entities classification and equivalent named entities retrieval using word and phrase representations. It is shown that a word or an expression’s context vector is an efficient feature to be used for predicting the type of a named entity. Distributed word representations are now claimed (and on a reasonable basis) to be one of the most promising distributional semantics models. In the described experiment on retrieving similar named entities the results go further than retrieving named entities of the same type or named entities-individuals of the same class: it is shown that equivalent variants of a named entity can be extracted. This result contributes to the task of unsupervised entities and semantic relations clustering and can be used for paraphrase search and automatic ontology population. The models were trained with word2vec on the Russian segment of parallel corpora used for statistical machine translation. Vector representations were constructed and evaluated for words, lexemes and noun phrases.