Classifying Referential and Non-referential It Using Gaze
Table 5: Confusion matrix for the best combined model (Weighted F1 = 68.8)
['NomAnaph', 'ClauseAnaph', 'Pleon', '[EMPTY]']
From the NLP perspective, the potential of gaze data to not only improve but also, to a certain extent, substitute text processing approaches is an exciting new frontier. An improvement of 13% over the majority baseline is achieved when combining the two but this difference is not a significant improvement over the individual best classifiers. A possible reason for this is that the gaze data and linguistic features encode similar information about the disambiguation of it and adding them together leads to overlap instead of complementation. In line with the observation of \newcitelee2016qa In our case, this is likely related to the small amount of data, to which we add 300 dimensions per word.