Acronyms—words formed from the initial letters of a phrase—are important for various natural language processing applications, including information retrieval and machine translation. While hand-crafted acronym dictionaries exist, they are limited and require frequent updates. We present a new machine-learning-based approach to automatically build an acronym dictionary from unannotated texts. This is the first such technique that specifically handles non-local acronyms , i.e., that can determine an acronym’s expansion even when the expansion does not appear in the same document as the acronym. Our approach automatically enhances the dictionary with contextual information to help address the acronym disambiguation task (selecting the most appropriate expansion for a given acronym in context), outperforming dictionaries built using prior techniques. We apply the approach to Modern Hebrew, a language with a long tradition of using acronyms, in which the productive morphology and unique orthography adds to the complexity of the problem.

中文翻译：

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首字母缩略词：识别、扩展和消歧

首字母缩略词——由短语的首字母组成的单词——对于各种自然语言处理应用程序都很重要，包括信息检索和机器翻译。虽然存在手工制作的首字母缩略词词典，但它们是有限的，需要经常更新。我们提出了一种新的基于机器学习的方法，可以从未注释的文本中自动构建首字母缩略词词典。这是第一个专门处理非本地首字母缩略词的此类技术，即，即使扩展没有出现在与首字母缩略词相同的文档中，也可以确定首字母缩略词的扩展。我们的方法使用上下文信息自动增强词典，以帮助解决首字母缩略词消歧任务（为上下文中的给定首字母缩略词选择最合适的扩展），优于使用先前技术构建的词典。