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  • 标题:PanParser: a Modular Implementation for Efficient Transition-Based Dependency Parsing
  • 本地全文:下载
  • 作者:Lauriane Aufrant ; Guillaume Wisniewski
  • 期刊名称:The Prague Bulletin of Mathematical Linguistics
  • 印刷版ISSN:0032-6585
  • 电子版ISSN:1804-0462
  • 出版年度:2018
  • 卷号:111
  • 期号:1
  • 页码:57-86
  • DOI:10.2478/pralin-2018-0007
  • 语种:English
  • 出版社:Walter de Gruyter GmbH
  • 摘要:We present PanParser, a Python framework dedicated to transition-based structured prediction, and notably suitable for dependency parsing. On top of providing an easy way to train state-of-the-art parsers, as empirically validated on UD 2.0, PanParser is especially useful for research purposes: its modular architecture enables to implement most state-of-the-art transition-based methods under the same unified framework (out of which several are already built-in), which facilitates fair benchmarking and allows for an exhaustive exploration of slight variants of those methods. PanParser additionally includes a number of fine-grained evaluation utilities, which have already been successfully leveraged in several past studies, to perform extensive error analysis of monolingual as well as cross-lingual parsing.
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