On the Arbitrariness of Lexical Categories

In this paper, we look at lexical categories and their predictability from a machine learning perspective. Starting from linguistic intuitions about predictability in three different domains, we show how standard techniques for analyzing classification tasks arive at a similar predictability scale. In the second part of the paper, we carry out machine learning experiments covering these domains and relate learnability results to the previous analysis. The IB1-IG classifier is found to be capable of learning in all three domains, although with varying degrees of success.