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2004. [http://www.lrec-conf.org/proceedings/lrec2004/pdf/219.pdf Evaluating Variants of the Lesk Approach for Disambiguating Words]. LREC, Portugal.</ref> has shown that the simplified Lesk algorithm can significantly outperform the original definition of the algorithm, both in terms of precision and efficiency. By evaluating the disambiguation algorithms on the Senseval-2 English all words data, they measure a 58% precision using the simplified Lesk algorithm compared to the only 42% under the original algorithm.
Note: Vasilescu et al. implementation considers a back-off strategy for words not covered by the algorithm, consisting of the most frequent sense defined in WordNet. This means that words for which all their possible meanings lead to zero overlap with current context or with other word definitions are by default assigned sense number one in WordNet."<ref>Agirre, Eneko & Philip Edmonds (eds.). 2006. [https://books.google.com/books?id=GLck75U20pAC
'''Simplified LESK Algorithm with smart default word sense (Vasilescu et al., 2004)'''<ref>Florentina Vasilescu, Philippe Langlais, and Guy Lapalme.
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==Lesk variants==
* Original Lesk (Lesk, 1986)
* Adapted/Extended Lesk (Banerjee and Pederson, 2002/2003): In the adaptive lesk algorithm, a word vector is created corresponds to every content word in the wordnet gloss. Concatenating glosses of related concepts in WordNet can be used to augment this vector. The vector contains the co-occurrence counts of words co-occurring with w in a large corpus. Adding all the word vectors for all the content words in its gloss creates the Gloss vector g for a concept. Relatedness is determined by comparing the gloss vector using the [[Cosine similarity]] measure.<ref>{{Cite book|
==See also==
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