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Word embeddings are powerful tools for many natural language processing tasks. In order to obtain embeddings useful for different tasks, it is necessary a large training corpus. In this work, we present a method based on the node2vec algorithm to learn word embeddings from a graph built using a corpus of word association norm in Spanish. The nodes of the graph correspond to the words in the corpus, whereas the

Representaciones vectoriales de palabras de un corpus de normas de asociación