Editing Word-sense disambiguation (section)

===Supervised methods===
[[Supervised learning|Supervised]] methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence, [[common sense]] and [[reasoning]] are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as [[feature selection]], parameter optimization, and [[ensemble learning]]. [[Support Vector Machines]] and [[memory-based learning]] have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged corpora for training, which are laborious and expensive to create.