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Linguistic and Statistical Extensions of Data Oriented Parsing
, 2006
"... This thesis explores certain linguistic and statistical extensions of Data-Oriented Parsing (DOP). The central idea in DOP is to analyse new input on the basis of a collection of fragment-probability pairs. In its simplest version, Tree-DOP, the fragments used are subparts of simple phrase structure ..."
Abstract
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This thesis explores certain linguistic and statistical extensions of Data-Oriented Parsing (DOP). The central idea in DOP is to analyse new input on the basis of a collection of fragment-probability pairs. In its simplest version, Tree-DOP, the fragments used are subparts of simple phrase structure trees. Resolving ambiguity (i.e. selecting the optimal analysis) involves identifying the Most Probable Parse (MPP). Though empirical evaluation has shown state-of-the-art results, the linguistic expressive mechanism of this model is very limited. In addition, the algorithm used to compute the MPP has been shown to suffer from several disadvantages. The aim of the thesis is two-fold. In the first part, we seek to explore how the linguis-tic dimension of DOP can be enhanced. To this end, we investigate how the framework can be applied to representations based on a richer annotation scheme, specifically that of Head-driven Phrase Structure Grammar (HPSG). This investigation culminates in the development of an HPSG-DOP model, which takes maximal advantage of the un-derlying formalism. The proposed model embodies a number of positive characteristics
Robust Efficient Parsing for Spoken Dialogue Processing
, 1998
"... ion (Johnson and Dorre, [39]) ffl x(A,B,f(A,B),g(A,h(B,i(C)))) =) x(A,B,f(,),g(,)) ffl parsewithweakening(Cat,P0,P,E0,E) :- weaken(Cat,WeakenedCat), parse(WeakenedCat,P0,P,E0,E), Cat=WeakenedCat. ffl Really helps! Ambiguity Packing ffl A parser should not construct all parse trees (exponential) ..."
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ion (Johnson and Dorre, [39]) ffl x(A,B,f(A,B),g(A,h(B,i(C)))) =) x(A,B,f(,),g(,)) ffl parsewithweakening(Cat,P0,P,E0,E) :- weaken(Cat,WeakenedCat), parse(WeakenedCat,P0,P,E0,E), Cat=WeakenedCat. ffl Really helps! Ambiguity Packing ffl A parser should not construct all parse trees (exponential) ffl Instead, a compact representation of all such parse trees are constructed -- grammar [42, 9] -- parse forest [76] -- packed structures [3] ffl Here: for every `result item' keep track of the lexical entry and references of other result items that were used to create it ffl Results in a lexicalized tree substitution grammar ffl which generates the input sentence with all its parse trees Bottom-up Inactive-chart Parser Item form: [i;X; j] Axioms: Goals: [0;S;n] Inference Rules: Scan [q i ;wi; qi+1 ] Complete [q k ;X1; q k 0][q k 0;X2; q k 00] : : : [q m0;Xl; qm] [q k ;X0; qm] X0 !X1:::Xl Bottom-up Inactive-chart Parser Inference Rules: Scan [q i ;wi; qi+...

