A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Word sense disambiguation with pattern learning and automatic feature selection
Natural Language Engineering
Statistical Word Sense Disambiguation in Contexts for Russian Nouns Denoting Physical Objects
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Word Sense Disambiguation: Algorithms and Applications
Word Sense Disambiguation: Algorithms and Applications
Parsing the SynTagRus treebank of Russian
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
StringNet as a computational resource for discovering and investigating linguistic constructions
EUCCL '10 Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics
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The research project reported in this paper aims at automatic extraction of linguistic information from contexts in the Russian National Corpus (RNC) and its subsequent use in building a comprehensive lexicographic resource - the Index of Russian lexical constructions. The proposed approach implies automatic context classification intended for word sense disambiguation (WSD) and construction identification (CxI). The automatic context processing procedure takes into account the following types of contextual information represented in the RNC multilevel annotation: lexical (lemma) tags (lex), morphological (grammatical) tags (gr), semantic (taxonomy) tags (sem), and combinations of the various types of tags. Multiple experiments on WSD and CxI are performed using RNC representative context samples for nouns. In each series of experiments we analyze (1) different context markers of meaning of target words and (2) constructions including context markers and target words.