Deep Entity Linking via Eliminating Semantic Ambiguity With BERT

IEEE Access

Yangchen Huang¹  Bin Zhou¹  Xiaoyao Yin¹  Long Lan¹  Yan Jia¹  Aiping Li¹ 

[1] College of Computer, National University of Defense Technology, Changsha, China;

关键词: Entity linking;    natural language processing (NLP);    bidirectional encoder representations from transformers (BERT);    deep neural network (DNN);   

DOI  :  10.1109/ACCESS.2019.2955498

来源: DOAJ

【 摘 要 】

Entity linking refers to the task of aligning mentions of entities in the text to their corresponding entries in a specific knowledge base, which is of great significance for many natural language process applications such as semantic text understanding and knowledge fusion. The pivotal of this problem is how to make effective use of contextual information to disambiguate mentions. Moreover, it has been observed that, in most cases, mention has similar or even identical strings to the entity it refers to. To prevent the model from linking mentions to entities with similar strings rather than the semantically similar ones, in this paper, we introduce the advanced language representation model called BERT (Bidirectional Encoder Representations from Transformers) and design a hard negative samples mining strategy to fine-tune it accordingly. Based on the learned features, we obtain the valid entity through computing the similarity between the textual clues of mentions and the entity candidates in the knowledge base. The proposed hard negative samples mining strategy benefits entity linking from the larger, more expressive pre-trained representations of BERT with limited training time and computing sources. To the best of our knowledge, we are the first to equip entity linking task with the powerful pre-trained general language model by deliberately tackling its potential shortcoming of learning literally, and the experiments on the standard benchmark datasets show that the proposed model yields state-of-the-art results.

【 授权许可】

Unknown