PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Constructing parse forests that include exactly the n-best PCFG trees
Pierre Boullier, Alexis Nasr and Benoit Sagot
In: International Conference on parsing technologies, 7 oct - 9 oct 2009, Paris France.


This paper describes and compares two algorithms that take as input a shared PCFG parse forest and produce shared forests that contain exactly the $n$ most likely trees of the initial forest. Such forests are suitable for subsequent processing, such as (some types of) reranking or LFG f-structure computation, that can be performed ontop of a shared forest, but that may have a high (e.g., exponential) complexity w.r.t.~the number of trees contained in the forest. We evaluate the performances of both algorithms on real-scale NLP forests generated with a PCFG extracted from the Penn Treebank.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:6701
Deposited By:Alexis Nasr
Deposited On:08 March 2010