POS Tagging and Its Applications for Mathematics - Text Analysis in Mathematics
POS Tagging and Its Applications for Mathematics - Text Analysis in Mathematics
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DOI：
10.1007/978-3-319-08434-3_16
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
U. Schöneberg;Wolfram Sperber
中科院分区：
文献类型：
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作者：
U. Schöneberg;Wolfram Sperber
科学出版物的内容分析是一项艰巨的任务，但对于科学信息服务来说却是一项有用而重要的任务。在古登堡时代，它是人类专家的领域；在数字时代，已经为它开发了许多基于机器的方法，例如图形分析工具和机器学习技术。自然语言处理(NLP)是半自动语音和语言处理的一种强大的机器学习方法，同样适用于数学。NLP的成熟方法必须根据数学的特殊需要进行调整，特别是在处理数学公式时。我们演示了一个数学感知的词性标记器，并简要概述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中为关键短语提取和分类开发的工具的使用。
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.