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

Maria Barrett, Joachim Bingel, Frank Keller, Anders Søgaard
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
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 partof-speech information. In this work, we bring these two ideas together and show
more » ... gether and show that given raw text, a dictionary, and eyetracking data obtained from naive participants reading text, we can train a weakly supervised PoS tagger using a secondorder HMM with maximum entropy emissions. The best model use type-level aggregates of eye-tracking data and significantly outperforms a baseline that does not have access to eye-tracking data.
doi:10.18653/v1/p16-2094 dblp:conf/acl/BarrettBKS16 fatcat:nujxnlajbre3hmg55emq2nvcgy