Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
Alessandro Raganato, José Camacho-Collados, Roberto Navigli · 2017
Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding.However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework.In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup.The results show that supervised systems clearly outperform knowledge-based models.Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat.Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.