<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an algorithm of automatic generation and labelling of the training collections based on the monosemous relatives concept. In this article we explore the limits of this algorithm: we employ it to harvest training collections for all ambiguous nouns, verbs and adjectives presented in RuWordNet thesaurus and then evaluate the quality of the obtained collections. We demonstrate that our approach can create high-quality labelled collections with almost full-coverage of the RuWordNet polysemous words. Furthermore, we show that our method can be applied to the Word-in-Context task.</p></abstract>

The limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an algorithm of automatic generation and labelling of the training collections based on the monosemous relatives concept. In this article we explore the limits of this algorithm: we employ it to harvest training collections for all ambiguous nouns, verbs and adjectives presented in RuWordNet thesaurus and then evaluate the quality of the obtained collections. We demonstrate that our approach can create high-quality labelled collections with almost full-coverage of the RuWordNet polysemous words. Furthermore, we show that our method can be applied to the Word-in-Context task.

All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an...

All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 90 - 107

Eingereicht: 15. Okt. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.2478/cait-2020-0049

Schlüsselwörter
Word Sense Disambiguation, Word-in-Context task, automatic annotation of training collections, monosemous relatives, Russian dataset, RuWordNet thesaurus

© 2020 Bolshina Angelina et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 90 - 107

Eingereicht: 15. Okt. 2020

Akzeptiert: 29. Okt. 2020

DOI: https://doi.org/10.2478/cait-2020-0049

SchlüsselwörterWord Sense Disambiguation, Word-in-Context task, automatic annotation of training collections, monosemous relatives, Russian dataset, RuWordNet thesaurus

© 2020 Bolshina Angelina et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Word Sense Disambiguation, Word-in-Context task, automatic annotation of training collections, monosemous relatives, Russian dataset, RuWordNet thesaurus