斯洛文尼亚语传记词典中的缩略语处理
Dealing with Abbreviations in the Slovenian Biographical Lexicon
摘要：
缩写对NLP系统来说是一个巨大的挑战，因为它们会导致标记化和词汇表外错误。它们还可以使文本可读性降低，特别是在参考印刷书籍中，它们被广泛使用。缩略语在资源匮乏的环境中尤其成问题，因为这些环境中的系统一开始就不太健全。在本文中，我们提出了一种新的方法来解决所造成的问题，在文本中的特定领域的缩写的高密度。我们应用这种方法的情况下，斯洛文尼亚的传记词典，并评估它在一个新开发的黄金标准数据集的51斯洛文尼亚传记。我们的缩写识别方法的性能显着优于常用的ad-hoc解决方案，特别是在识别看不见的缩写。我们还提出了一种在上下文中扩展已识别缩写的方法并给出了结果。
英文摘要：
Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
