Nothing
library(udpipe)
library(word2vec)
data(brussels_reviews, package = "udpipe")
x <- subset(brussels_reviews, language == "nl")
x <- tolower(x$feedback)
model <- word2vec::word2vec(x = x, dim = 15, iter = 20)
emb <- as.matrix(model)
pred <- predict(model, emb["bus",], type = "nearest", top_n = 10)
pred
# similarity in the library
cross <- rowSums(sqrt(crossprod(t(emb), emb["bus",]) / ncol(emb)))
head(sort(cross, decreasing = TRUE))
# cosine similarity
cosine <- Matrix::rowSums(proxyC::simil(emb, emb["bus",,drop = FALSE]))
head(sort(cosine, decreasing = TRUE))
# they are very similar but different
cor(cross, cosine, use = "pair")
cor(cross, cosine, use = "pair", method = "spearman")
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