Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic
  Lexical Resources
Stefano Melacci Achille Globo Leonardo Rigutini
Supervised models for Word Sense Disambiguation (WSD) currently yield to
state-of-the-art results in the most popular benchmarks. Despite the recent
introduction of Word Embeddings and Recurrent Neural Networks to design
powerful context-related features, the interest in improving WSD models using
Semantic Lexical Resources (SLRs) is mostly restricted to knowledge-based
approaches. In this paper, we enhance "modern" supervised WSD models exploiting
two popular SLRs: WordNet and WordNet Domains. We propose an effective way to
introduce semantic features into the classifiers, and we consider using the SLR
structure to augment the training data. We study the effect of different types
of semantic features, investigating their interaction with local contexts
encoded by means of mixtures of Word Embeddings or Recurrent Neural Networks,
and we extend the proposed model into a novel multi-layer architecture for WSD.
A detailed experimental comparison in the recent Unified Evaluation Framework
(Raganato et al., 2017) shows that the proposed approach leads to supervised
models that compare favourably with the state-of-the art.