Editing Word-sense disambiguation (section)

===Dictionary- and knowledge-based methods===
The [[Lesk algorithm]]{{sfn|Lesk|1986|pp=24–26}} is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary). A similar approach<ref>{{Cite book|last1=Diamantini|first1=C.|last2=Mircoli|first2=A.|last3=Potena|first3=D.|last4=Storti|first4=E.|title=2015 International Conference on Collaboration Technologies and Systems (CTS) |chapter=Semantic disambiguation in a social information discovery system |s2cid=13260353|date=2015-06-01|pages=326–333|doi=10.1109/CTS.2015.7210442|isbn=978-1-4673-7647-1}}</ref> searches for the shortest path between two words: the second word is iteratively searched among the definitions of every semantic variant of the first word, then among the definitions of every semantic variant of each word in the previous definitions and so on. Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word.

An alternative to the use of the definitions is to consider general word-sense [[relatedness]] and to compute the [[semantic similarity]] of each pair of word senses based on a given lexical knowledge base such as [[WordNet]]. [[Graph (discrete mathematics)|Graph-based]] methods reminiscent of [[spreading activation]] research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods{{sfn|Navigli|Velardi|2005|pp=1063–1074}} or even outperforming them on specific domains.{{sfn|Navigli|Litkowski|Hargraves|2007|pp=30–35}}{{sfn|Agirre|Lopez de Lacalle|Soroa|2009|pp=1501–1506}} Recently, it has been reported that simple [[Connectivity (graph theory)|graph connectivity measures]], such as [[Degree (graph theory)|degree]], perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base.{{sfn|Navigli|Lapata|2010|pp=678–692}} Also, automatically transferring [[knowledge]] in the form of [[semantic relation]]s from Wikipedia to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.{{sfn|Ponzetto|Navigli|2010|pp=1522–1531}}

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).