Update MAN

Author: koheiw

NEWS.md

@@ -1,8 +1,8 @@
 ## Changes in v0.6.1
 
 - Mention doc2vec in package description.
-- Add `perplexity()` to asses models' the goodness-of-fit.
-- Save quanteda's internal docvars in the `textmodel_doc2vec` objects.
+- Add `perplexity()` to asses models' the goodness-of-fit to data.
+- Save **quanteda**'s internal docvars in the `textmodel_doc2vec` objects.
 - Add `group` to `as.matrix()` to average sentence or paragraph vectors from the same documents.
 
 ## Changes in v0.6.0
@@ -52,4 +52,4 @@
 - Recreate `word2vec()` with new argument names and object structures.
 - Create `lda()` to train word vectors using Latent Semantic Analysis.
 - Add `similarity()` and `analogy()` functions using **proxyC**.
-- Add `data_corpus_news2014` that contain 20,000 news summaries as package data.
+- Add `data_corpus_news2014` that contain 20,000 news summaries as package data.

R/utils.R

@@ -41,7 +41,7 @@ analogy <- function(formula) {
 #' @param mode specify the type of resulting object.
 #' @return a `matrix` of cosine similarity scores when `mode = "numeric"` or of 
 #'   words sorted in descending order by the similarity scores when `mode = "character"`.
-#'   When `words` is a named numeric vector, word (or document) vectors are weighted and summed 
+#'   When `targets` is a named numeric vector, word (or document) vectors are weighted and summed 
 #'   before computing similarity scores.
 #' @export
 #' @seealso [probability()]
@@ -102,7 +102,7 @@ similarity <- function(x, targets, layer = c("words", "documents"),
 #' @param ... passed to `as.matrix()`.
 #' @return a matrix of words or documents sorted in descending order by the probability 
 #'   scores when `mode = "character"`; a matrix of the probability scores when `mode = "numeric"`.
-#'   When `words` is a named numeric vector, probability scores are weighted by
+#'   When `targets` is a named numeric vector, probability scores are weighted by
 #'   the values.
 #' @export
 #' @seealso [similarity()]

R/word2vec.R

@@ -226,7 +226,7 @@ print.textmodel_doc2vec <- function(x, ...) {
 #' @param normalize if `TRUE`, returns normalized vectors.
 #' @param layer the layer from which the vectors are extracted.
 #' @param group \[experimental\] average sentence or paragraph vectors from the same document. 
-#'   Silently ignored when `layer = "word"`. 
+#'   Silently ignored when `layer = "words"`. 
 #' @param ... not used.
 #' @return a matrix that contain the word or document vectors in rows.
 #' @export