information. This combined model is applied to several domains and at last the domain-specific sentiment lexicons are united to create an improved version of the Russian sentiment lexicon in the generalized domain of products. 1

be generated from the electronic version of Zaliznjak's dictionary (Ilola and Mustajoki 1989). From this data we plan to develop a wide-coverage morphosyntactic lemmatizer and tagger for Russian.

A quantitative analysis of the English lexicon was done in the paper. The three electronic dictionaries are under examination: the English Wiktionary, WordNet, and the Russian Wiktionary. It was calculated the quantity of English words and meanings (senses) in these dictionaries. The distribution

Research on the structure of an individual’s mental lexicon has shown that, among the set of concepts belonging to a single semantic field, there is typically one particular concept that stands out. The processes of storage, identification, and retrieval of this concept are faster and more

In this paper we describe a DomEx sentiment lexicon extractor, where a new approach for domain-specific sentiment lexicon extraction is implemented. Sentiment lexicon extraction is based on the machine learning model comprising a set of statistical and linguistic features. The extraction model

We discuss the possibility to link the lexicon of an NLP system with a formal ontology in an attempt to construct a semantic analyzer of natural language texts. The work is carried out on the material of sports news published in Russian media. 1

This study explores the structure of the mental lexicon and the processing of Russian verbal morphology by three groups of speakers, adult American learners of Russian, Russian children aged 4-6 with normal linguistic development, and Russian children aged 4-7 with specific language impairment (SLI

at development and/or run time. There are four linguistic counterparts, corresponding to the major categories of lexical information developed in our system: lexicon, knowledge base, corpora and Russian language processing software.

1. The Big Picture: Why study metonymy in grammar? Slide 7: Russian example of grammatical metonymy vehicle target suffix vehicle class target class vehicle ex target ex брюхан person with a large part whole (и)а/ян noun noun брюхо belly belly Slide 8: Czech example of grammatical metonymy vehicle

Abstract. This paper presents an automatic methodology we used in an experiment to acquire a morphological lexicon for the Slovak language, and the lexicon we obtained. This methodology extends and refines approaches which have proven efficient, e.g., for the acquisition of French verbs or Croatian