Entity Recognition at First Sight: Improving NER with Eye Movement Information
This work addresses named entity recognition for NLP applications by incorporating eye-tracking data, but it is incremental as it builds on existing methods with a novel feature integration.
The authors tackled named entity recognition by augmenting a state-of-the-art neural model with eye movement features from gaze data, resulting in improved performance over baselines in both individual and cross-domain evaluations.
Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.