  When we read, our brain processes language and generates cognitive processing
data such as gaze patterns and brain activity. These signals can be recorded
while reading. Cognitive language processing data such as eye-tracking features
have shown improvements on single NLP tasks. We analyze whether using such
human features can show consistent improvement across tasks and data sources.
We present an extensive investigation of the benefits and limitations of using
cognitive processing data for NLP. Specifically, we use gaze and EEG features
to augment models of named entity recognition, relation classification, and
sentiment analysis. These methods significantly outperform the baselines and
show the potential and current limitations of employing human language
processing data for NLP.
