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Regularized Least-Squares for Parse Ranking

Evgeni Tsivtsivadze, Tapio Pahikkala, Sampo Pyysalo, Jorma Boberg, Aleksandr Mylläri, Tapio Salakoski, Regularized Least-Squares for Parse Ranking. In: In proceedings of the 6th International Symposiumon Intelligent Data Analysis, Lecture Notes in Computer Science, 464–474, Springer-Verlag Berlin Heidelberg 2005, 2005.

Abstract:


We present an adaptation of the Regularized Least-Squares algorithm for the rank learning problem and an application of the method to reranking of the parses produced by the Link Grammar (LG) dependency parser. We study the use of several grammatically motivated features extracted from parses and evaluate the ranker with individual features and the combination of all features on a set of biomedical sentences annotated for syntactic dependencies. Using a parse goodness function based on the F-score, we demonstrate that our method produces a statistically significant increase in rank correlation from 0.18 to 0.42 compared to the built-in ranking heuristics of the LG parser. Further, we analyze the performance of our ranker with respect to the number of sentences and parses per sentence used for training and illustrate that the method is applicable to sparse datasets, showing improved performance with as few as 100 training sentences.

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BibTeX entry:

@INPROCEEDINGS{inpTsPaPyBoMySa05a,
  title = {Regularized Least-Squares for Parse Ranking},
  booktitle = {In proceedings of  the 6th International Symposiumon Intelligent Data Analysis},
  author = {Tsivtsivadze, Evgeni and Pahikkala, Tapio and Pyysalo, Sampo and Boberg, Jorma and Mylläri, Aleksandr and Salakoski, Tapio},
  volume = {Lecture Notes in Computer Science},
  number = {3646},
  publisher = {Springer-Verlag Berlin Heidelberg 2005},
  pages = {464–474},
  year = {2005},
  keywords = {Parse ranking, RLS Ranking},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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