Automatic Terminology Extraction Using a Dependency-Graph in NLP
Automatic Terminology Extraction Using a Dependency-Graph in NLP
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DOI：
10.1007/978-3-030-73603-3_38
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发表时间：
2020
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影响因子：
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通讯作者：
Yusuke Kimura;Kazuma Kusu;K. Hatano;Tokiya Baba
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文献类型：
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作者：
Yusuke Kimura;Kazuma Kusu;K. Hatano;Tokiya Baba
自动术语提取（ATE）是一种用于提取表示数据集的短语的技术。翻译专业书籍和文件需要这种技巧。现有的方法关注术语往往由两个或多个单一名词组成这一事实。但是，它不处理修饰关系，只处理单个名词之间的共现关系。此外，我们必须考虑这样一个事实，即当我们提出一种新的方法时，被定义为术语的短语往往会在另一个句子中得到解释。在这项研究中，我们提出了一种从数据集中提取术语的方法，该方法考虑了依赖分析获得的修改关系。特别是，我们提出了如何提取特征，使我们能够从句子的依赖结构中区分短语是否是术语。
Automatic Terminology Extraction (ATE) is a technique for extracting phrases representing a dataset. This technique is required for translating specialistic books and documents. An existing method focused on the fact that terminologies tend to be composed of two or more single nouns. However, it does not deal with modification relations but only co-occurrence relations among single nouns. Moreover, we have to consider the fact that phrases defined as terminology tend to be explained in another sentence when we propose a novel approach. In this study, we propose a method for extracting terminologies from a dataset considering the modification relations obtained by dependency analysis. In particular, we propose how to extract features enabling us to distinguish whether or not the phrase is terminology from a dependency structure of a sentence.