  When processing a text, humans and machines must disambiguate between
different uses of the pronoun it, including non-referential, nominal anaphoric
or clause anaphoric ones. In this paper, we use eye-tracking data to learn how
humans perform this disambiguation. We use this knowledge to improve the
automatic classification of it. We show that by using gaze data and a
POS-tagger we are able to significantly outperform a common baseline and
classify between three categories of it with an accuracy comparable to that of
linguisticbased approaches. In addition, the discriminatory power of specific
gaze features informs the way humans process the pronoun, which, to the best of
our knowledge, has not been explored using data from a natural reading task.
