Entity Linking Based on Sentence Representation

Abstract

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning of the sentences and reduce the noise introduced by different contexts of the same mention in a document. This model first uses the symmetry of the Siamese network to learn the sentence similarity. Then, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance.

An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance

“…Following the previous research [4,62], GCNCS only considers non-NIL mentions which correspond to correct entities in KB. Therefore, micro-averaged accuracy is selected for evaluation, which only concerns the correctly linked mentions.…”