diff --git a/R/word2vec.R b/R/word2vec.R
index 28d29f5..781b9d7 100644
--- a/R/word2vec.R
+++ b/R/word2vec.R
@@ -106,10 +106,9 @@
 #' x <- subset(x, grepl(xpos, pattern = paste(LETTERS, collapse = "|")))
 #' x$text <- sprintf("%s/%s", x$lemma, x$xpos)
 #' x <- subset(x, !is.na(lemma))
-#' x <- paste.data.frame(x, term = "text", group = "doc_id", collapse = " ")
-#' x <- x$text
+#' x <- split(x$text, list(x$doc_id, x$sentence_id))
 #' 
-#' model <- word2vec(x = x, dim = 15, iter = 20, split = c(" ", ".\n?!"))
+#' model <- word2vec(x = x, dim = 15, iter = 20)
 #' emb   <- as.matrix(model)
 #' nn    <- predict(model, c("cuisine/NN", "rencontrer/VB"), type = "nearest")
 #' nn
diff --git a/man/word2vec.Rd b/man/word2vec.Rd
index bca8f80..ee8ce9d 100644
--- a/man/word2vec.Rd
+++ b/man/word2vec.Rd
@@ -144,10 +144,9 @@ x <- subset(brussels_reviews_anno, language == "fr")
 x <- subset(x, grepl(xpos, pattern = paste(LETTERS, collapse = "|")))
 x$text <- sprintf("\%s/\%s", x$lemma, x$xpos)
 x <- subset(x, !is.na(lemma))
-x <- paste.data.frame(x, term = "text", group = "doc_id", collapse = " ")
-x <- x$text
+x <- split(x$text, list(x$doc_id, x$sentence_id))
 
-model <- word2vec(x = x, dim = 15, iter = 20, split = c(" ", ".\n?!"))
+model <- word2vec(x = x, dim = 15, iter = 20)
 emb   <- as.matrix(model)
 nn    <- predict(model, c("cuisine/NN", "rencontrer/VB"), type = "nearest")
 nn
diff --git a/man/word2vec.character.Rd b/man/word2vec.character.Rd
index 05c95ac..6a4aaa9 100644
--- a/man/word2vec.character.Rd
+++ b/man/word2vec.character.Rd
@@ -148,10 +148,9 @@ x <- subset(brussels_reviews_anno, language == "fr")
 x <- subset(x, grepl(xpos, pattern = paste(LETTERS, collapse = "|")))
 x$text <- sprintf("\%s/\%s", x$lemma, x$xpos)
 x <- subset(x, !is.na(lemma))
-x <- paste.data.frame(x, term = "text", group = "doc_id", collapse = " ")
-x <- x$text
+x <- split(x$text, list(x$doc_id, x$sentence_id))
 
-model <- word2vec(x = x, dim = 15, iter = 20, split = c(" ", ".\n?!"))
+model <- word2vec(x = x, dim = 15, iter = 20)
 emb   <- as.matrix(model)
 nn    <- predict(model, c("cuisine/NN", "rencontrer/VB"), type = "nearest")
 nn