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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.10384 |
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| _version_ | 1866918159146024960 |
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| author | Sung, Hakyung Kyle, Kristopher |
| author_facet | Sung, Hakyung Kyle, Kristopher |
| contents | Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ASC analyzer: A Python package for measuring argument structure construction usage in English texts Sung, Hakyung Kyle, Kristopher Computation and Language Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores. |
| title | ASC analyzer: A Python package for measuring argument structure construction usage in English texts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.10384 |