More or less supervised supersense tagging of Twitter
Anders Johannsen, Dirk Hovy, Héctor Martínez Alonso, Barbara Plank, Anders Søgaard · 2014
We present two Twitter datasets annotated with coarse-grained word senses (supersenses), as well as a series of experiments with three learning scenarios for supersense tagging: weakly supervised learning, as well as unsupervised and supervised domain adaptation.We show that (a) off-the-shelf tools perform poorly on Twitter, (b) models augmented with embeddings learned from Twitter data perform much better, and (c) errors can be reduced using type-constrained inference with distant supervision from WordNet.