This article describes two different word sense disambiguation (WSD) systems, one applicable to parallel corpora and requiring aligned wordnets and the other one, knowledge poorer, albeit more relevant for real applications, relying on unsupervised learning methods and only monolingual data (text and wordnet). Comparing performances of word sense disambiguation systems is a very difficult evaluation task when different sense inventories are used and even more difficult when the sense distinctions are not of the same granularity. However, as we used the same sense inventory, the performance of the two WSD systems can be objectively compared and we bring evidence that multilingual WSD is more precise than monolingual WSD
ROMANSEVAL: Results for Italian by SENSE
FEDERICI, STEFANO;
2000-01-01
Abstract
This article describes two different word sense disambiguation (WSD) systems, one applicable to parallel corpora and requiring aligned wordnets and the other one, knowledge poorer, albeit more relevant for real applications, relying on unsupervised learning methods and only monolingual data (text and wordnet). Comparing performances of word sense disambiguation systems is a very difficult evaluation task when different sense inventories are used and even more difficult when the sense distinctions are not of the same granularity. However, as we used the same sense inventory, the performance of the two WSD systems can be objectively compared and we bring evidence that multilingual WSD is more precise than monolingual WSD
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