R: A Scored Co-occurrence Matrix of Nouns Denoting Goods...

DSM_GoodsMatrix {wordspace} | R Documentation

## A Scored Co-occurrence Matrix of Nouns Denoting Goods (wordspace)

### Description

A pre-scored verb-object co-occurrence matrix for 240 target nouns denoting goods and the 3 feature verbs own, buy and sell. This matrix is useful for illustrating the application and purpose of dimensionality reduction techniques.

### Usage

    DSM_GoodsMatrix

### Format

A numeric matrix with 240 rows corresponding to target nouns denoting goods and 4 columns, corresponding to

`own`, `buy`, `sell`:

  association scores for co-occurrences of the nouns with the verbs own, buy and sell

`fringe`:

  an indicator of how close each point is to the “fringe” of the data set (ranging from 0 to 1)

### Details

Co-occurrence data are based on verb-object dependency relations in the British National Corpus, obtained from `DSM_VerbNounTriples_BNC`. Only nouns that co-occur with all three verbs are included in the data set.

The co-occurrence matrix is weighted with non-sparse log-likelihood (`simple-ll`) and an additional logarithmic transformation (`log`). Row vectors are not normalized.

The fringeness score in column `fringe` indicates how close a data point is to the fringe of the data set. Values are distance quantiles based on PCA-whitened Manhattan distance from the centroid. For example, `fringe >= .8` characterizes 20% of points that are closest to the fringe. Fringeness is mainly used to select points to be labelled in plots or to take stratified samples from the data set.

### Examples

    DSM_GoodsMatrix[c("time", "goods", "service"), ]

[Package wordspace version 0.2-6 Index]