An Eye-tracking Study of Named Entity Annotation

Takenobu Tokunaga, Hitoshi Nishikawa, Tomoya Iwakura


Abstract
Utilising effective features in machine learning-based natural language processing (NLP) is crucial in achieving good performance for a given NLP task. The paper describes a pilot study on the analysis of eye-tracking data during named entity (NE) annotation, aiming at obtaining insights into effective features for the NE recognition task. The eye gaze data were collected from 10 annotators and analysed regarding working time and fixation distribution. The results of the preliminary qualitative analysis showed that human annotators tend to look at broader contexts around the target NE than recent state-of-the-art automatic NE recognition systems and to use predicate argument relations to identify the NE categories.
Anthology ID:
R17-1097
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
758–764
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_097
DOI:
10.26615/978-954-452-049-6_097
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PDF:
https://doi.org/10.26615/978-954-452-049-6_097