2D- or 3D-Approximation of the neighborhood of a given word/sentence

1 2 3 |

`x` |
a character vector of |

`n` |
the number of neighbors to be computed |

`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 |

`start.lines` |
(3d plot only) whether lines shall be drawn between |

`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 |

Attempts to create an image of the semantic neighborhood (based on cosine similarity) to a
given word, sentence/ document, or vector. An attempt is made to depict this subpart of the LSA
space in a two- or three-dimensional plot.

To achieve this, either a Principal Component
Analysis (PCA) or a Multidimensional Scaling (MDS) is computed to preserve the interconnections between all the words in this
neighborhod as good as possible. Therefore, it is important to note that the image created from
this function is only the best two- or three-dimensional approximation to the true LSA space subpart.

For creating pretty plots showing the similarity structure within this neighborhood best, set `connect.lines="all"`

and `col="rainbow"`

For three-dimensional plots: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,* 211-240.

Mardia, K.V., Kent, J.T., & Bibby, J.M. (1979). *Multivariate Analysis*, London: Academic Press.

`cosine`

,
`neighbors`

,
`multicos`

,
`plot_wordlist`

,
`plot3d`

,
`princomp`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(wonderland)
## Standard Plot
plot_neighbors("cheshire",n=20,tvectors=wonderland)
## Pretty Plot
plot_neighbors("cheshire",n=20,tvectors=wonderland,
connect.lines="all",col="rainbow")
plot_neighbors(compose("mad","hatter",tvectors=wonderland),
n=20, connect.lines=2,tvectors=wonderland)
``` |

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