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{{Short description|Natural language processing algorithm}}
Lesk, M. (1986). [http://portal.acm.org/citation.cfm?id=318728&dl=GUIDE,ACM&coll=GUIDE&CFID=103485667&CFTOKEN=64768709 Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone]. In SIGDOC '86: Proceedings of the 5th annual international conference on Systems documentation, pages 24-26, New York, NY, USA. ACM.
</ref> It operates on the premise that words within a given context are likely to share a common meaning. This algorithm compares the dictionary definitions of an ambiguous word with the words in its surrounding context to determine the most appropriate sense. Variations, such as the Simplified Lesk algorithm, have demonstrated improved precision and efficiency. However, the Lesk algorithm has faced criticism for its sensitivity to definition wording and its reliance on brief glosses. Researchers have sought to enhance its accuracy by incorporating additional resources like thesauruses and syntactic models.
==Overview==
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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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The COMPUTEOVERLAP function returns the number of words in common between two sets, ignoring function words or other words on a stop list. The original Lesk algorithm defines the context in a more complex way.
==Criticisms
Unfortunately, Lesk’s approach is very sensitive to the exact wording of definitions, so the absence of a certain word can radically change the results. Further, the algorithm determines overlaps only among the glosses of the senses being considered. This is a significant limitation in that dictionary glosses tend to be fairly short and do not provide sufficient vocabulary to relate fine-grained sense distinctions.
A lot of work has appeared offering different modifications of this algorithm. These works use other resources for analysis (thesauruses, synonyms dictionaries or morphological and syntactic models): for instance, it may use such information as synonyms, different derivatives, or words from definitions of words from definitions.<ref>Alexander Gelbukh, Grigori Sidorov. [https://www.gelbukh.com/CV/Publications/2004/NTI-2004-senses.htm Automatic resolution of ambiguity of word senses in dictionary definitions] (in Russian). J. Nauchno-Tehnicheskaya Informaciya (NTI), ISSN 0548-0027, ser. 2, N 3, 2004, pp. 10–15.</ref>
==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|
There are a lot of studies concerning Lesk and its extensions:<ref>Roberto Navigli. [http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf ''Word Sense Disambiguation: A Survey''], ACM Computing Surveys, 41(2), 2009, pp. 1–69.</ref>
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* Nastase and Szpakowicz, 2001;
* Gelbukh and Sidorov, 2004.
▲==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|last=Banerjee|first=Satanjeev|last2=Pedersen|first2=Ted|date=2002-02-17|title=An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet|journal=Computational Linguistics and Intelligent Text Processing|series=Lecture Notes in Computer Science|language=en|publisher=Springer, Berlin, Heidelberg|pages=136–145|doi=10.1007/3-540-45715-1_11|isbn=978-3540457152|citeseerx=10.1.1.118.8359}}</ref>
==See also==
{{Commons}}
{{Portal|Linguistics}}
* [[Word-sense disambiguation
==References==
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