POS标记及其在数学中的应用
POS Tagging and its Applications for Mathematics
摘要：
科技出版物的内容分析是一项重要的工作，也是科技信息服务的重要内容。在古腾堡时代，这是人类专家的领域;在数字时代，许多基于机器的方法，例如，自然语言处理（Natural Language Processing，NLP）是半自动语音和语言处理的一种强大的机器学习方法，它也适用于数学。已经建立的NLP方法必须根据数学的特殊需要进行调整，特别是处理数学公式。我们展示了一个认知语法的词性标注器，并简要介绍了我们对数学出版物的NLP方法的适应。我们展示了在数据库zbMATH中开发的用于关键短语提取和分类的工具的使用。
英文摘要：
Content analysis of scientific publications is a nontrivial task, but a useful and important one for scientific information services. In the Gutenberg era it was a domain of human experts; in the digital age many machine-based methods, e.g., graph analysis tools and machine-learning techniques, have been developed for it. Natural Language Processing (NLP) is a powerful machine-learning approach to semiautomatic speech and language processing, which is also applicable to mathematics. The well established methods of NLP have to be adjusted for the special needs of mathematics, in particular for handling mathematical formulae. We demonstrate a mathematics-aware part of speech tagger and give a short overview about our adaptation of NLP methods for mathematical publications. We show the use of the tools developed for key phrase extraction and classification in the database zbMATH.
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