This paper describes a fuzzy-based methodology in order to aggregate outcomes of distinct wordsense disambiguation algorithms. The latter are derived from standard Lesk algorithm, its WorldNet extension and new interpretations of the set-intersection that accounts for various WordNet domain knowledge and part-of-speech conversion. The fuzzy preference model imitates the fuzzy Borda voting scheme. The developed algorithms are evaluated according to SenseEval 2 competition dataset, where a clear improvement to the baseline algorithm has been testified.
Authors:
Farooghian F., Oussalah M., Gilian E.
Publication type:
A4 Article in conference proceedings
Place of publication:
Data Science and Knowledge Engineering for Sensing Decision Support. Proceedings of the 13th International FLINS Conference (FLINS 2018)
Keywords:
fuzzy preference, semantic similarity, word sense disambiguation, WordNet
Published:
Full citation:
A fuzzy based approach for wordsense disambiguation using morphological transformation and domain link knowledge, F. Farooghian (), M. Oussalah (), and E. Gilian (), Data Science and Knowledge Engineering for Sensing Decision Support. October 2018, 617-625, https://doi.org/10.1142/9789813273238_0079
DOI:
https://doi.org/10.1142/9789813273238_0079
Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020042119543
