Constructing semantic representations using the MDL principle

Words receive a signiicant part of their meaning from use in communicative settings. The formal mechanisms of lexical acquisition, as they apply to rich situational settings, may also be studied in the limited case of corpora of written texts. This work constitutes an approach to deriving semantic representations for lexemes using techniques from statistical induction. In particular, a number of variations on the MDL principle were applied to selected sample sets and their innuence on emerging theories of word meaning explored. We found that by changing the deenition of description length for data and theory-which is equivalent to diierent encodings of data and theory-we may customize the emerging theory, augmenting and altering frequency eeects. Also the innuence of stochastic properties of the data on the size of the theory has been demonstrated. The results consist in a set of distributional properties of lexemes, which reeect cognitive distinctions in the meaning of words.