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Parameter transfer across domains for word sense disambiguation
Publikationstyp
Conference Paper
Date Issued
2017
Sprache
English
Institut
TORE-URI
Start Page
1
End Page
8
Citation
11th International Conference on Recent Advances in Natural Language Processing, RANLP 2017: 1-8 (2017)
Contribution to Conference
Publisher DOI
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.
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.
DDC Class
600: Technik
More Funding Information
This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under the project K3 (13N13548).