R: Similarity Ratings for 351 Noun Pairs (wordspace)

WordSim353 {wordspace} | R Documentation

## Similarity Ratings for 351 Noun Pairs (wordspace)

### Description

A database of human similarity ratings for 351 English noun pairs, collected by Finkelstein et al. (2002) and annotated with semantic relations (similarity vs. relatedness) by Agirre et al. (2009).

### Usage

    WordSim353

### Format

A data frame with 351 rows and the following 6 columns:

`word1`

  first noun (character)

`word2`

  second noun (character)

`score`

  average similarity rating by human judges on scale from 0 to 10 (numeric)

`relation`

  semantic relation between first and second word (factor, see Details below)

`similarity`

  whether word pair belongs to the similarity subset (logical)

`relatedness`

  whether word pair belongs to the relatedness subset (logical)

The nouns are given as disambiguated lemmas in the form `<headword>_N`.

### Details

The data set is known as `WordSim353` because it originally consisted of 353 noun pairs. One duplicate entry (money–cash) as well as the trivial combination tiger–tiger (which may have been included as a control item) have been omitted in the present version, however.

The following semantic relations are distinguished in the `relation` variable: `synonym`, `antonym`, `hypernym`, `hyponym`, `co-hyponym`, `holonym`, `meronym` and `other` (topically related or completely unrelated).

Note that the similarity and relatedness subsets are not disjoint, because they share 103 unrelated noun pairs (semantic relation `other` and score below 5.0).

### Source

Similarity ratings (Finkelstein et al. 2002): http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/

Semantic relations (Agirre et al. 2009): http://alfonseca.org/eng/research/wordsim353.html

### References

Agirre, Eneko, Alfonseca, Enrique, Hall, Keith, Kravalova, Jana, Pasca, Marius, and Soroa, Aitor (2009). A study on similarity and relatedness using distributional and WordNet-based approaches. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2009), pages 19–27, Boulder, Colorado.

Finkelstein, Lev, Gabrilovich, Evgeniy, Matias, Yossi, Rivlin, Ehud, Solan, Zach, Wolfman, Gadi, and Ruppin, Eytan (2002). Placing search in context: The concept revisited. ACM Transactions on Information Systems, 20(1), 116–131.

### Examples

    head(WordSim353, 20)

    table(WordSim353$relation) # semantic relations

    # split into "similarity" and "relatedness" subsets
    xtabs(~ similarity + relatedness, data=WordSim353)

[Package wordspace version 0.2-6 Index]