An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages (L18-1)
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Dmitry Ustalov, Denis Teslenko, Alexander Panchenko, Mikhail Chernoskutov, Chris Biemann, Simone Paolo Ponzetto
| Challenge: | Existing systems for word sense disambiguation are limited to the Russian language and lack of resources to address the problem. |
| Approach: | They propose an unsupervised system for word sense disambiguation that uses a traditional vector space model to estimate the most similar word sense corresponding to its context. |
| Outcome: | The proposed system outperforms the sparse mode on all datasets according to the adjusted Rand index. |
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