Dependency Parsers for Persian
نویسندگان
چکیده
We present two dependency parsers for Persian, MaltParser and MSTParser, trained on the Uppsala PErsian Dependency Treebank. The treebank consists of 1,000 sentences today. Its annotation scheme is based on Stanford Typed Dependencies (STD) extended for Persian with regard to object marking and light verb contructions. The parsers and the treebank are developed simultanously in a bootstrapping scenario. We evaluate the parsers by experimenting with different feature settings. Parser accuracy is also evaluated on automatically generated and gold standard morphological features. Best parser performance is obtained when MaltParser is trained and optimized on 18,000 tokens, achieving 68.68% labeled and 74.81% unlabeled attachment scores, compared to 63.60% and 71.08% for labeled and unlabeled attachment score respectively by optimizing MSTParser.
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