Implicit Entity Recognition, Classification and Linking in Tweets

Hawre Hosseini
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引用次数: 9

Abstract

Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.
推文中的隐式实体识别、分类和链接
将短语链接到知识库实体是一个被称为实体链接的过程,并且已经被广泛地用于各种内容类型,如tweets。实体链接的一个主要步骤是识别和/或分类可以消除歧义并链接到知识库实体的短语,即命名实体识别和分类。然而,与常见的实体识别和链接系统不同,我们的目标是识别、分类和链接隐含提及的实体,因此缺乏表面形式,以适当的知识库条目。换句话说,我们工作的目标是在没有明确提及的情况下识别和识别推文的核心实体;这个过程被称为隐式命名实体识别和链接。
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