Word embeddings represent word meaning in the form of a vector; however, the encoded
information varies depending on the parameters the vector has been trained with. This paper
analyzes how two parameters, context size and symmetry, influence word embedding
information and aims to find if there exists a single distributional parametrization for capturing
semantic similarity as well as relatedness. The models were trained with GloVe with different
parametrizations; then, they were quantitatively ...
Word embeddings represent word meaning in the form of a vector; however, the encoded
information varies depending on the parameters the vector has been trained with. This paper
analyzes how two parameters, context size and symmetry, influence word embedding
information and aims to find if there exists a single distributional parametrization for capturing
semantic similarity as well as relatedness. The models were trained with GloVe with different
parametrizations; then, they were quantitatively evaluated through a similarity task, using
WordSim-353 (for relatedness) and SimLex-999 (for semantic similarity) as benchmarks. The
results show a minimal variation when manipulating some of the analyzed parameters, in
particular between symmetric and asymmetric contexts, which leads us to conclude that it is
not necessary to train models with large contexts for achieving good performance.
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