Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data

Abstract

For many of the world’s languages, there are no or very few linguistically annotated resources. On the other hand, raw text, and often also dictionaries, can be harvested from the web for many of these languages, and part-of-speech taggers can be trained with these resources. At the same time, previous research shows that eye-tracking data, which can be obtained without explicit annotation, contains clues to part-of-speech information. In this work, we bring these two ideas together and show that given raw text, a dictionary, and eye-tracking data obtained from naive participants reading text, we can train a weakly supervised PoS tagger using a second-order HMM with maximum entropy emissions. The best model use type-level aggregates of eye-tracking data and signifi-cantly outperforms a baseline that does not have access to eye-tracking data.