# Find nearest neighbors

### Description

Returns the n nearest words to a given word or sentence/document

### Usage

1 | ```
neighbors(x,n,tvectors=tvectors,breakdown=FALSE)
``` |

### Arguments

`x` |
a character vector of |

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

`tvectors` |
the semantic space in which the computation is to be done (a numeric matrix where every row is a word vector) |

`breakdown` |
if |

### Details

The format of `x`

should be of the kind `x <- "word1 word2 word3"`

instead of

`x <- c("word1", "word2", "word3")`

if sentences/documents are used as input. This allows for simple copy&paste-inserting of text.

To import a document *Document.txt* to from a directory for comparisons, set your working
directory to this directory using `setwd()`

. Then use the following command lines:

`fileName1 <- "Alice_in_Wonderland.txt"`

`x <- readChar(fileName1, file.info(fileName1)$size)`

.

Since `x`

can also be chosen to be any vector of the active LSA Space, this function can be
combined with `compose()`

to compute neighbors of complex expressions (see examples)

### Value

A named numeric vector. The neighbors are given as names of the vector, and their respective cosines to the input as vector entries.

### Author(s)

Fritz Günther

### References

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.

Dennis, S. (2007). How to use the LSA Web Site. In T. K. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), *Handbook of Latent
Semantic Analysis* (pp. 35-56). Mahwah, NJ: Erlbaum.

### See Also

`cosine`

,
`plot_neighbors`

,
`compose`

### Examples

1 2 3 4 5 6 | ```
data(wonderland)
neighbors("cheshire",n=20,tvectors=wonderland)
neighbors(compose("mad","hatter",method="Add",tvectors=wonderland),
n=20,tvectors=wonderland)
``` |