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