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Master Thesis Year : 2013

Solution Ranking for a Symbolic Parser

Abstract

Ranking is one of the methods used to improve data-driven parsers. The task of the ranker is to define a function that will assign a score to each parse candidate tree obtained in the parsing process. We want to apply such models to resolve ambiguity problems in LEOPAR, a grammar-driven parser. LEOPAR produces a high number of parse solutions especially when the size if the input sentence grows. We want to choose the most suitable solutions among the parse candidates proposed by LEOPAR. We have to notice that a scoring system exists already in LEOPAR. It is based onsome handcrafted rules. We present in this document a ranking solution for LEOPAR based on statistical techniques. The candidate parses provided by LEOPAR are used as input for our ranker. We test our approach on the Sequoia TreeBank and obtain an improvement of the system compared to the handcrafted rules.
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Dates and versions

hal-01864774 , version 1 (30-08-2018)

Identifiers

  • HAL Id : hal-01864774 , version 1

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Tatiana Ekeinhor. Solution Ranking for a Symbolic Parser. Cognitive Sciences. 2013. ⟨hal-01864774⟩
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