More than meets the eye: Study of Human Cognition in Sense Annotation

Abstract

Word Sense Disambiguation (WSD) approaches have reported good accuracies in recent years. However, these approaches can be classified as weak AI systems. According to the classical definition, a strong AI based WSD system should perform the task of sense disambiguation in the same manner and with similar accuracy as human beings. In order to accomplish this, a detailed understanding of the human techniques employed for sense disambiguation is necessary. Instead of building yet another WSD system that uses contextual evidence for sense disambiguation, as has been done before, we have taken a step back - we have endeavored to discover the cognitive faculties that lie at the very core of the human sense disambiguation technique. In this paper, we present a hypothesis regarding the cognitive sub-processes involved in the task of WSD. We support our hypothesis using the experiments conducted through the means of an eye-tracking device. We also strive to find the levels of difficulties in annotating various classes of words, with senses. We believe, once such an in-depth analysis is performed, numerous insights can be gained to develop a robust WSD system that conforms to the principle of strong AI.

Publication
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies