通过自组织检索的隐式实体链接

Hawre Hosseini, Tam T. Nguyen, E. Bagheri
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引用次数: 7

摘要

在一个称为实体链接的过程中,已经探索了文档中明确观察到的短语到知识库实体的系统链接。然而,本文的目标是识别和实体链接那些未被提及但在文档中隐含的实体,更具体地说,在tweet中。这个过程被称为隐式实体链接。与之前基于知识库中的相关内容为每个实体构建表示的工作不同,我们建议通过确定tweet与在线发布的用户生成内容的相关性来执行隐式实体链接,并因此间接执行实体链接。我们将这个问题表述为一个特别的文档检索过程,其中输入查询是需要隐式链接的tweet,文档空间是与知识库实体相关的用户生成内容的集合。我们系统地将我们的工作与最先进的基线进行比较,并表明我们的方法能够提供统计上显着的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implicit Entity Linking Through Ad-Hoc Retrieval
The systematic linking of explicitly-observed phrases within a document to entities of a knowledge base has already been explored in a process known as entity linking. The objective of this paper, however, is to identify and entity link those entities that are not mentioned but are implied within a document, more specifically within a tweet. This process is referred to as implicit entity linking. Unlike prior work that build a representation for each entity based on its related content in the knowledge base, we propose to perform implicit entity linking by determining how a tweet is related to user-generated content posted online and as such indirectly perform entity linking. We formulate this problem as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of user-generated content related to the entities of the knowledge base. We systematically compare our work with the state-of-the-art baseline and show that our method is able to provide statistically significant improvements.
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