Unification-based algorithms We have seen that treating features, such as person-number agreement, as part of the category of a phrase results in a large.

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Presentation transcript:

Unification-based algorithms We have seen that treating features, such as person-number agreement, as part of the category of a phrase results in a large multiplication of the number of rules in a grammar: S  NP VP S  NP3Sg VP3Sg S  NPNon3Sg VPNon3Sg

A better approach Decouple category (type) from features: treat features as properties of categories Use unification to combine feature structures S  NP [Num= , Per=  ] VP [Num= , Per=  ]

Feature structures A feature-structure is a set of feature-value pairs A feature-structure is also called an Attribute-Value Matrix (AVM) [] feature-1value-1 feature-2value-2 …… feature-Nvalue-N

Examples [(Cat,NP),(Number,SG),(Person,3)] [CatNP Agr[NumberSG Person3 ]

Sharing [CatS Head[ Agr1:[ NumberSG Person3 ] Subject[ Agr [1] ] ]

Unification examples on board.

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