An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
This addresses the problem of word sense disambiguation for under-resourced languages, but it is incremental as it builds on existing methods with a new mode.
The paper tackles word sense disambiguation for under-resourced languages by presenting Watasense, an unsupervised system with sparse and dense modes; the dense mode, using synset embeddings, substantially outperforms the sparse mode on Russian datasets according to the adjusted Rand index.
In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes of operation. The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context. The dense mode, instead, uses synset embeddings to cope with the sparsity problem. We describe the architecture of the present system and also conduct its evaluation on three different lexical semantic resources for Russian. We found that the dense mode substantially outperforms the sparse one on all datasets according to the adjusted Rand index.