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
- Editors:
- Ruslan Mitkov, Galia Angelova
- 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
- Cite (ACL):
- Takenobu Tokunaga, Hitoshi Nishikawa, and Tomoya Iwakura. 2017. An Eye-tracking Study of Named Entity Annotation. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 758–764, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- An Eye-tracking Study of Named Entity Annotation (Tokunaga et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_097