Kateryna Tymoshenko

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Kateryna Tymoshenko is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Wikipedia-based WSD for multilingual frame annotation Frame annotation
FrameNet-Wikipedia mapping
Multilingual FrameNets
Word sense disambiguation
Artificial Intelligence English 2013 Many applications in the context of natural language processing have been proven to achieve a significant performance when exploiting semantic information extracted from high-quality annotated resources. However, the practical use of such resources is often biased by their limited coverage. Furthermore, they are generally available only for English and few other languages. We propose a novel methodology that, starting from the mapping between FrameNet lexical units and Wikipedia pages, automatically leverages from Wikipedia new lexical units and example sentences. The goal is to build a reference data set for the semi-automatic development of new FrameNets. In addition, this methodology can be adapted to perform frame identification in any language available in Wikipedia. Our approach relies on a state-of-the-art word sense disambiguation system that is first trained on English Wikipedia to assign a page to the lexical units in a frame. Then, this mapping is further exploited to perform frame identification in English or in any other language available in Wikipedia. Our approach shows a high potential in multilingual settings, because it can be applied to languages for which other lexical resources such as WordNet or thesauri are not available. © 2012 Elsevier B.V. All rights reserved. 0 0
Disambiguation and filtering methods in using web knowledge for coreference resolution Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24 English 2011 We investigate two publicly available web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution (CR) engine. We extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a CR system. We show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto and Poesio 2009). We propose, therefore, a number of solutions to reduce the amount of noise coming from web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. Our evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves our system's performance by 2-3 percentage points. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved. 0 0
Acquiring thesauri from wikis by exploiting domain models and lexical substitution ESWC English 2010 0 0