collections to search in combination with word embedding techniques. Combining these would aid the best performing collection … collection selection model based on word embedding using Word2Vec approach that learns the similarity between the current and … uses word embedding for collection selection by learning from past queries.  …

embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering … data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word …

para inferir el contenido (temas) de los artículos, y Word Embedding (implementado con el modelo Skip-gram) y K-Medias para …. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding …