论文标题
联合嵌入在句子级别链接的指定实体中
Joint Embedding in Named Entity Linking on Sentence Level
论文作者
论文摘要
命名实体链接是将文档中的模棱两可的提及映射到知识库中的实体。鉴于文档中有多个候选实体可以提及的事实，指定的实体链接是具有挑战性的。当文档中多次出现时，很难将提及链接，因为围绕提及的外观存在冲突。此外，由于给定的培训数据集很小，因此很难，因为它是手动将提及与其映射实体联系起来的原因。在文献中，有许多报道的研究，其中最近的嵌入方法从文档级别从培训数据集中学习实体的向量。为了解决这些问题，我们专注于如何将实体链接在句子级别上的提及，这减少了文档中同一提及的不同外观引入的噪音，而牺牲了不足的信息来使用。我们通过最大化从知识图中学到的关系提出了一种新的统一嵌入方法。我们在实验研究中证实了我们方法的有效性。
Named entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document. It is difficult to link a mention when it appears multiple times in a document, since there are conflicts by the contexts around the appearances of the mention. In addition, it is difficult since the given training dataset is small due to the reason that it is done manually to link a mention to its mapping entity. In the literature, there are many reported studies among which the recent embedding methods learn vectors of entities from the training dataset at document level. To address these issues, we focus on how to link entity for mentions at a sentence level, which reduces the noises introduced by different appearances of the same mention in a document at the expense of insufficient information to be used. We propose a new unified embedding method by maximizing the relationships learned from knowledge graphs. We confirm the effectiveness of our method in our experimental studies.
