Paper: A Cognitive Cost Model of Annotations Based on Eye-Tracking Data

ACL ID P10-1118
Title A Cognitive Cost Model of Annotations Based on Eye-Tracking Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

We report on an experiment to track com- plex decision points in linguistic meta- data annotation where the decision behav- ior of annotators is observed with an eye- tracking device. As experimental con- ditions we investigate different forms of textual context and linguistic complexity classes relative to syntax and semantics. Our data renders evidence that annotation performance depends on the semantic and syntactic complexity of the decision points and, more interestingly, indicates that full- scale context is mostly negligible – with the exception of semantic high-complexity cases. We then induce from this obser- vational data a cognitively grounded cost model of linguistic meta-data annotations and compare it with existing non-cognitive models. Our data reveals that the cogni- tive...