Pioneering Granularity: Advancing Native Language Identification in Ultra-Short EAP Texts

Abstract

This study addresses the challenge of Native Language Identification (NLI) in ultra-short English for Academic Purposes (EAP) texts by proposing an innovative two-stage recognition method. Conventional views suggest that ultra-short texts lack sufficient linguistic features for effective NLI. However, we have found that even in such brief texts, subtle linguistic cues—such as syntactic structures, lexical choices, and grammatical errors—can still reveal the author's native language background. Our approach involves fine-tuning the granularity of first language (L1) labels and refining deep learning models to more accurately capture the subtle differences in second language (L2) English texts written by individuals from similar cultural backgrounds. To validate the effectiveness of this method, we designed and conducted a series of scientific experiments using advanced Natural Language Processing (NLP) techniques. The results demonstrate that models adjusted for granular L1 distinctions exhibit greater sensitivity and accuracy in identifying language variations caused by nuanced cultural differences. Furthermore, this method is not only applicable to ultra-short texts but can also be extended to texts of varying lengths, offering new perspectives and tools for handling diverse language inputs. By integrating in-depth linguistic analysis with advanced computational techniques, our research opens up new possibilities for enhancing the performance and adaptability of NLI models in complex linguistic environments. It also provides fresh insights for future efforts aimed at optimizing the capture of linguistic features.