Part of speech tagging of grammatical features related to L2 Chinese development: A case analysis of Stanza in the L2 writing context.

Abstract

Grammatical complexity has received extensive attention in second language acquisition. Although computational tools have been developed to analyze grammatical complexity, most relevant studies investigated this construct in the context of English as a second language. In response to an increasing number of L2 Chinese learners, it is important to extend the investigation of grammatical complexity in L2 Chinese. To promote relevant research, we evaluated the new computational tool, Stanza, on its accuracy of part-of-speech tagging for L2 Chinese writing. We particularly focused on eight grammatical features closely related to L2 Chinese development. Then, we reported the precisions, recalls, and F-scores for the individual grammatical features and offered a qualitative analysis of systematic tagging errors. In terms of the precision, three features have high rates, over 90% (i.e., ba and bei markers, classifiers, -de as noun modifier marker). For recall, four features have high rates, over 90% (i.e., aspect markers, ba and bei markers, classifiers, -de as noun modifier marker). Overall, based on the F-scores, Stanza has a good tagging performance on ba and bei markers, classifiers, and -de as a noun modifier marker. This evaluation provides research implications for scholars who plan to use this computational tool to study L2 Chinese development in second language acquisition or applied linguistics in general.