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Modified from Diane Litman's version of Steve Bird's notes
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Rule-Based Tagger
•The Linguistic Complaint
–Where is the linguistic knowledge of a tagger?
–Just a massive table of numbers
–Aren’t there any linguistic insights that could emerge from the data?
–Could thus use handcrafted sets of rules to tag input sentences, for example, if input follows a
determiner tag it as a noun.
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Slide modified from Massimo Poesio's
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The Brill tagger
•An example of Transformation-Based Learning
–Basic idea: do a quick job first (using frequency), then revise it using contextual rules.
–Painting metaphor from the readings
•Very popular (freely available, works fairly well)
•A supervised method: requires a tagged corpus

Brill Tagging: In more detail
•Start with simple (less accurate) rules…learn better ones from tagged corpus
–Tag each word initially with most likely POS
–Examine set of transformations to see which improves tagging decisions compared to tagged corpus
–Re-tag corpus using best transformation
–Repeat until, e.g., performance doesn’t improve
–Result: tagging procedure (ordered list of transformations) which can be applied to new, untagged
text

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Slide modified from Massimo Poesio's
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An example
•Examples:
–They are expected to race tomorrow.
–The race for outer space.
•Tagging algorithm:
1.Tag all uses of “race” as NN (most likely tag in the Brown corpus)
•They are expected to race/NN tomorrow
•the race/NN for outer space
2.Use a transformation rule to replace the tag NN with VB for all uses of “race” preceded by the
tag TO:
•They are expected to race/VB tomorrow
•the race/NN for outer space

Example Rule Transformations
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Sample Final Rules
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