Using Word Association Norms to Measure Corpus Representativeness

An obvious way to measure how representative a corpus is for the language environment of a person would be to observe this person over a longer period of time, record all written or spoken input, and compare this data to the corpus in question. As this is not very practical, we suggest here a more indirect way to do this. Previous work suggests that people’s word associations can be derived from corpus statistics. These word associations are known to some degree as psychologists have collected them from test persons in large scale experiments. The output of these experiments are tables of word associations, the so-called word association norms. In this paper we assume that the more representative a corpus is for the language environment of the test persons, the better the associations generated from it should match people’s associations. That is, we compare the corpus-generated associations to the association norms collected from humans, and take the similarity between the two as a measure of corpus representativeness. To our knowledge, this is the first attempt to do so.