Improving the Recognizability of Syntactic Relations Using Contextualized Examples

نویسندگان

  • Aditi S. Muralidharan
  • Marti A. Hearst
چکیده

A common task in qualitative data analysis is to characterize the usage of a linguistic entity by issuing queries over syntactic relations between words. Previous interfaces for searching over syntactic structures require programming-style queries. User interface research suggests that it is easier to recognize a pattern than to compose it from scratch; therefore, interfaces for non-experts should show previews of syntactic relations. What these previews should look like is an open question that we explored with a 400-participant Mechanical Turk experiment. We found that syntactic relations are recognized with 34% higher accuracy when contextual examples are shown than a baseline of naming the relations alone. This suggests that user interfaces should display contextual examples of syntactic relations to help users choose between different relations.

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تاریخ انتشار 2014