Natural Language Processing for Teaching Ancient Languages

K. Schulz
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Abstract

Konstantin Schulz shows various applications of natural language processing (NLP) to the field of Classics, especially to Latin texts. He addresses different levels of linguistic analysis while also highlighting educational benefits and important theoretical pitfalls, especially in vocabulary learning. NLP can solve some problems reasonably well, like tailoring exercises to the learners' current state of knowledge. However, some tasks still prove to be too difficult for machines at the moment, e.g. reliable and highly accurate parsing of syntax for historical languages.
自然语言处理在古代语言教学中的应用
康斯坦丁·舒尔茨展示了自然语言处理(NLP)在古典文学领域的各种应用,特别是在拉丁文本方面。他讨论了不同层次的语言分析,同时也强调了教育的好处和重要的理论缺陷,特别是在词汇学习方面。NLP可以很好地解决一些问题,比如根据学习者当前的知识状况定制练习。然而,目前对于机器来说,有些任务仍然过于困难,例如对历史语言的可靠和高度准确的语法解析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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