An Unsupervised Word Sense Disambiguation System forUnder-Resourced Languages
Table 1: Results on RUSSE’2018 (Adjusted Rand Index).
['[BOLD] Method AdaGram', '[BOLD] Method AdaGram', '[BOLD] bts-rnc 0.22', '[BOLD] wiki-wiki 0.39', '[BOLD] Avg. 0.23']
We compare the evaluation results obtained for the Sparse and Dense approaches with three baselines: the AdaGram model (AdaGram), the same sense for all the instances per lemma (One) and one sense per instance (Singletons). The column Avg. consists of the weighted average of the datasets w.r.t. the number of instances. The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words. Thus, we recommend using the dense approach in further studies. Although the AdaGram approach trained on a large text corpus showed better results according to the weighted average, this result does not transfer to languages with less available corpus size.