Parsing Syntactic and Semantic Dependencies for Multiple Languages with A Pipeline Approach
BibTeX
@MISC{Ren_parsingsyntactic,
author = {Han Ren and Donghong Ji and Jing Wan and Mingyao Zhang},
title = {Parsing Syntactic and Semantic Dependencies for Multiple Languages with A Pipeline Approach},
year = {}
}
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Abstract
This paper describes a pipelined approach for CoNLL-09 shared task on joint learning of syntactic and semantic dependencies. In the system, we handle syntactic dependency parsing with a transition-based approach and utilize MaltParser as the base model. For SRL, we utilize a Maximum Entropy model to identify predicate senses and classify arguments. Experimental results show that the average performance of our system for all languages achieves 67.81 % of macro F1 Score, 78.01% of syntactic accuracy, 56.69 % of semantic labeled F1, 71.66 % of macro precision and 64.66 % of micro recall. 1
