Data-driven Measurement of Child Language Development with Simple Syntactic Templates

نویسندگان

  • Shannon Lubetich
  • Kenji Sagae
چکیده

When assessing child language development, researchers have traditionally had to choose between easily computable metrics focused on superficial aspects of language, and more expressive metrics that are carefully designed to cover specific syntactic structures and require substantial and tedious labor. Recent work has shown that existing expressive metrics for child language development can be automated and produce accurate results. We go a step further and propose that measurement of syntactic development can be performed automatically in a completely data-driven way without the need for definition of language-specific inventories of grammatical structures. As a crucial step in that direction, we show that four simple feature templates are as expressive of language development as a carefully crafted standard inventory of grammatical structures that is commonly used and has been validated empirically.

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تاریخ انتشار 2014