Word sense disambiguation aims to identify which meaning of a word is present in a given usage. Gathering word sense annotations is a laborious and difficult task. Several methods have been proposed to gather sense annotations using large numbers of untrained annotators, with mixed results. We propose three new annotation methodologies for gathering word senses where untrained annotators are allowed to use multiple labels and weight the senses. Our findings show that given the appropriate annotation task, untrained workers can obtain at least as high agreement as annotators in a controlled setting, and in aggregate generate equally as good of a sense labeling.
More recently, REF ) 13 compared three annotation methodologies for gathering word sense labels on AMT.
Embracing Ambiguity: A Comparison of Annotation Methodologies for Crowdsourcing Word Sense Labels
NAACL
Computer Science