Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement
This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61{\%}, 52.26{\%}, 55.71{\%} for LAS, MLAS and BLEX respectively.
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Dependency ParsingPart-Of-Speech TaggingSimilar Papers 제목 키워드 기반
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