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Lexical Dependency Parsing. Chris Brew. OhioState University. Using Lexical Dependencies. Lexical information crucial to parser success Original version is Magerman’s SPATTER Each is simpler than the last Often also with improved performance. The Task.

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Lexical dependency parsing

Lexical Dependency Parsing

Chris Brew

OhioState University


Using lexical dependencies
Using Lexical Dependencies

  • Lexical information crucial to parser success

    • Original version is Magerman’s SPATTER

    • Each is simpler than the last

    • Often also with improved performance


The task
The Task

  • Generate trees as in Wall Street Journal part of Penn Treebank

  • Collins provides new statistical model for P(T|S)

  • PCFGs used rules, DOP used fragments, LR used parser states

  • This uses Bigram Lexical Dependencies plus a few extras


The components of the model
The components of the model

  • A model of base NPs P(B|S)

    • Obtained using bigram statistics and POS tags

  • A model of dependencies P(D|S,B)

  • A bijective mapping which can interconvert between

    • Trees

    • Pairings of base NP structure and dependencies


A parse tree
A parse tree

  • Base NPs

    [John Smith][the president][IBM] [his resignation] [yesterday]

  • Treebank is linguistically odd here


Propagating head words

S(announced)

NP(Smith)

VP(announced)

NP(Smith)

NP(president)

VBD

NP(resignation)

NP

NNP

NNP

NP

PP(of)

announced

PRP$

NN

NN

John

Smith

DT

NN

IN

NP

his

resignation

yesterday

the

president

of

NNP

IBM

Propagating head words

  • Small set of rules propagate heads


Extracted structure

VBD

VP

NP

VP

VBD

NP

announced

[his

Resignation]

[yesterday]

Extracted structure

  • Base NPs plus Dependencies

  • Dependencies labelled with triplesof nonterminals

    NB. Not all dependencies shown here

NP

S

VP

NP

NP

NP

[John

Smith]

[the

president]

of

[IBM]


Statistical model
Statistical model

  • Gives probabilities to dependencies

  • So the probability of a rule like VP -> VBD NP NP, which involves two dependencies, is made from the probabilities of the components.

VBD

VP

NP

VBD

VP

NP

announced

[his

Resignation]

[yesterday]


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