Difference between revisions of "Sense Disambiguation"

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= Automatic Strategies for Sense Disambiguation =
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== Similarity Algorithms ==
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A broad category of disambiguation schemes is to start with a similarity metric that gives a score between every two synsets and seeks to maximize that score across all of the possible synsets.
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=== Lesk ===
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When inquisitive readers encounter a word they don't know or a word that is used in a way that is unfamiliar to them, they look up the term in a dictionary.  Confronted with multiple options for a term, they find that definition (or gloss) that best fits the context the word appeared in.  Using this intuition, Mike Lesk developed an elegant scheme whereby the sense of a word that has the largest number of overlaps the other senses is chosen.
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This work has been extended by Siddharth Patwardhan and Ted Pedersen to use context vectors for each of the words in a WordNet gloss to form a basis for a new similarity measure.
  
= Automatic Strategies for Sense Disambiguation =  
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=== Computational Issues ===
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As the number of synsets increases and the
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== Statistical Methods ==  
  
== Lesk Algorithm ==
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=== Hiding a Semantic Hierarchy in a Markov Model ===
  
This algorithm works by comparing the definitions of terms within WordNet.
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Although Abney and Light focused on the problem of disambiguation in the context of selectional restrictions, their approach is very similar to our approach. They seek

Revision as of 15:55, 24 August 2006

Automatic Strategies for Sense Disambiguation

Similarity Algorithms

A broad category of disambiguation schemes is to start with a similarity metric that gives a score between every two synsets and seeks to maximize that score across all of the possible synsets.

Lesk

When inquisitive readers encounter a word they don't know or a word that is used in a way that is unfamiliar to them, they look up the term in a dictionary. Confronted with multiple options for a term, they find that definition (or gloss) that best fits the context the word appeared in. Using this intuition, Mike Lesk developed an elegant scheme whereby the sense of a word that has the largest number of overlaps the other senses is chosen.

This work has been extended by Siddharth Patwardhan and Ted Pedersen to use context vectors for each of the words in a WordNet gloss to form a basis for a new similarity measure.

Computational Issues

As the number of synsets increases and the


Statistical Methods

Hiding a Semantic Hierarchy in a Markov Model

Although Abney and Light focused on the problem of disambiguation in the context of selectional restrictions, their approach is very similar to our approach. They seek