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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/2512.15552 |
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| _version_ | 1866911324381904896 |
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| author | Ellis, Dakota Bakikerali, Samy Chen, Wanshan Dinh, Bao Le, Uyen |
| author_facet | Ellis, Dakota Bakikerali, Samy Chen, Wanshan Dinh, Bao Le, Uyen |
| contents | Traditional linguists have proposed the use of a General Service List (GSL) to assist new language learners in identifying the most important words in English. This process requires linguistic expertise, subjective input, and a considerable amount of time. We attempt to create our own GSL and evaluate its practicality against the industry standard (The NGSL). We found creating a Specialized Word List (SWL), or a word list specific to a subset of the overall corpus, to be the most practical way for language-learners to optimize the process. The SWL's that we created using our model outperformed the industry standard, reaching the 95% coverage required for language comprehension with fewer words comparatively. By restricting the SWL process to objective criteria only, it can be automated, scaled, and tailored to the needs of language-learners across the globe. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Data to Dialogue: Unlocking Language for All Ellis, Dakota Bakikerali, Samy Chen, Wanshan Dinh, Bao Le, Uyen Computation and Language Traditional linguists have proposed the use of a General Service List (GSL) to assist new language learners in identifying the most important words in English. This process requires linguistic expertise, subjective input, and a considerable amount of time. We attempt to create our own GSL and evaluate its practicality against the industry standard (The NGSL). We found creating a Specialized Word List (SWL), or a word list specific to a subset of the overall corpus, to be the most practical way for language-learners to optimize the process. The SWL's that we created using our model outperformed the industry standard, reaching the 95% coverage required for language comprehension with fewer words comparatively. By restricting the SWL process to objective criteria only, it can be automated, scaled, and tailored to the needs of language-learners across the globe. |
| title | From Data to Dialogue: Unlocking Language for All |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.15552 |