Joint Embedding in Named Entity Linking on Sentence Level
This work addresses entity linking for ambiguous mentions in documents, but it is incremental as it adapts embedding methods to the sentence level.
The paper tackles the problem of named entity linking at the sentence level, where ambiguous mentions must be mapped to entities in a knowledge base, and proposes a unified embedding method that maximizes relationships from knowledge graphs, showing effectiveness in experiments.
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.