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2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2014)
Beijing, China
July 13, 2014 to July 15, 2014
ISSN: 2168-3034
ISBN: 978-1-4799-3844-5
pp: 57-62
ABSTRACT
This paper presents a simple and effective approachto improve dependency parsing by exploiting multiplefeature-sets. Traditionally, features are extracted by applyingthe feature templates to all the word pairs(first-order features)and word tuples(second-order features). In this pa per, we showthat exploiting different feature templates for different wordpairs and word tuples achieves significant improvement overbaseline parsers. First, we train a text chunker using a freelyavailable implementation of the first-order linear conditionalrandom fields model. Then we build a clause-chunk tree for agiven sentence based on chunking information and punctuationmarks. Finally, we extract features for dependency parsingaccording to multiple feature-sets. We extend the projectiveparsing algorithms of McDonald[20] and Carreras[1] for ourcase, experimental results show that our approach significantlyoutperform the baseline systems without increasing complexity. Given correct chunking information, we improve from baselineaccuracies of 91.36% and 92.20% to 93.19% and 93.89%, respectively.
INDEX TERMS
Feature extraction, Heuristic algorithms, Accuracy, Inference algorithms, Approximation algorithms, Training, Tagging,semi-supervised methods, dependency parsing
CITATION
Xianchao Zhang, Dong Du, Xinyue liu, Wenxin Liang, "Multiple Feature-Sets Method for Dependency Parsing", 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 57-62, 2014, doi:10.1109/PAAP.2014.30
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