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        <title>Parameter Transfer across Domains for Word Sense Disambiguation</title>
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            <namePart type="given">Galia</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Parameter Transfer across Domains for Word Sense Disambiguation
%A Abualhaija, Sallam
%A Tahmasebi, Nina
%A Forin, Diane
%A Zimmermann, Karl-Heinz
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F abualhaija-etal-2017-parameter
%X 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.
%R 10.26615/978-954-452-049-6_001
%U https://doi.org/10.26615/978-954-452-049-6_001
%P 1-8

Markdown (Informal)

[Parameter Transfer across Domains for Word Sense Disambiguation](https://doi.org/10.26615/978-954-452-049-6_001) (Abualhaija et al., RANLP 2017)

• Parameter Transfer across Domains for Word Sense Disambiguation (Abualhaija et al., RANLP 2017)

ACL

• Sallam Abualhaija, Nina Tahmasebi, Diane Forin, and Karl-Heinz Zimmermann. 2017. Parameter Transfer across Domains for Word Sense Disambiguation. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 1–8, Varna, Bulgaria. INCOMA Ltd..