2D or 3DPlot of mutual word similarities to a given list of words
1 2 3 
x 
a character vector of 
dims 
the dimensionality of the plot; set either 
method 
the method to be applied; either a Principal Component Analysis ( 
connect.lines 
(3d plot only) the number of closest associate words each word is connected with via line. Setting 
axes 
(3d plot only) whether axes shall be included in the plot 
box 
(3d plot only) whether a box shall be drawn around the plot 
cex 
(2d Plot only) A numerical value giving the amount by which plotting text should be magnified relative to the default. 
tvectors 
the semantic space in which the computation is to be done (a numeric matrix where every row is a word vector) 
breakdown 
if 
alpha 
(3d plot only) a vector of one or two numerics between 0 and 1 specifying the luminance of 
col 
(3d plot only) a vector of one or two characters specifying the color of 
... 
additional arguments which will be passed to 
Computes all pairwise similarities within a given list of words. On this similarity matrix, a Principal Component Analysis (PCA) or a Multidimensional Sclaing (MDS) is applied to get a two or threedimensional solution that best captures the similarity structure. This solution is then plottet.
For creating pretty plots showing the similarity structure within this list of words best, set connect.lines="all"
and col="rainbow"
see plot3d
: this function is called for the side effect of drawing the plot; a vector of object IDs is returned.
plot_neighbors
also gives the coordinate vectors of the words in the plot as a data frame
Fritz Günther
Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211240.
Mardia, K.V., Kent, J.T., & Bibby, J.M. (1979). Multivariate Analysis, London: Academic Press.
cosine
,
neighbors
,
multicos
,
plot_neighbors
,
plot3d
,
princomp
1 2 3 4 5 6 7  data(wonderland)
## Standard Plot
words < c("alice","hatter","queen","knight","hare","cheshire")
plot_wordlist(words,tvectors=wonderland,method="MDS",dims=2)

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