USING MULTI-STIMULUS WORD ASSOCIATIONS FOR MEASURING CORPUS REPRESENTATIVENESS
A method for measuring corpus representativeness is introduced. It assumes that human intuitions about language are based on the analysis of the spoken and written language an individual has perceived during his or her lifetime. We try to replicate simple forms of such intuitions, namely multi-stimulus associations, by statistically analysing text corpora. We then compare the corpus-derived intuitions to intuitions as collected from human subjects and measure the agreement between the two. We finally claim that the higher the agreement, the better a corpus represents the language environment of the test persons, i.e. the more representative the corpus is.