Word Sense Disambiguation Based on Constrained RandomWalks in Linked Semantic Networks
Arkadiusz Janz, Maciej Piasecki · 2019
Word Sense Disambiguation remains a challenging NLP task.Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses.Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios.In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks.We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used.The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Składnica, KPWr) datasets.