Classification of Taxonomical Relationship by Word Embedding

Kazuki Omine, Incheon Paik
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引用次数: 3

Abstract

In recent years, while Internet has brought various technological evolutions, users have been required to collect, select and integrate information according to a purpose. Based on this background, ontology that systemizes knowledge of the target world has been received a lot of attention. As a method of automatically constructing a super-sub relation which is a one of the important concept of ontology, there is a method of using a Lexico-syntactic pattern and a word dictionary. However, there are problems that cannot be classified correctly because it does not consider semantic relation of words so that cannot deal with words not existed in the dictionary. Therefore, a method to classify super-sub relation using a wedge product of word vectors is proposed to solve the problem. As a result, it has been confirmed that the effectiveness of the research to get higher precision/recall than that of the baseline method.
基于词嵌入的分类关系分类
近年来,互联网带来了各种各样的技术变革,用户需要根据目的来收集、选择和整合信息。基于这一背景,将目标世界的知识系统化的本体受到了广泛的关注。作为一种自动构造上下级关系的方法(上下级关系是本体的重要概念之一),有一种使用词典句法模式和词字典的方法。但是,由于没有考虑词的语义关系,无法处理字典中不存在的词,存在不能正确分类的问题。为此,提出了一种利用词向量的楔形积对上下级关系进行分类的方法。实验结果表明,该方法能够获得比基线方法更高的查准率/查全率。
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