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Parameter Transfer across Domains for Word Sense Disambiguation

Conference paper
Authors Sallam Abualhajia
Nina Tahmasebi
Diane Forin
Karl-Heinz Zimmermann
Published in To appear in Proceedings of Recent Advances in Natural Language Processing 2017
Publication year 2017
Published at Department of Swedish
Language en
Links lml.bas.bg/ranlp2017/RANLP2017_proc...
Subject categories Computational linguistics, Language Technology (Computational Linguistics)

Abstract

Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words in a given context with a maximum semantic similarity. Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the word sense disambiguation problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing, hence, it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.

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